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Kangshun Li · Jin Li
Yong Liu · Aniello Castiglione (Eds.)

Communications in Computer and Information Science 575

Computational Intelligence
and Intelligent Systems
7th International Symposium, ISICA 2015
Guangzhou, China, November 21–22, 2015
Revised Selected Papers

123
Communications
in Computer and Information Science 575
Commenced Publication in 2007
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Pontifical Catholic University of Rio de Janeiro (PUC-Rio),
Rio de Janeiro, Brazil
Phoebe Chen
La Trobe University, Melbourne, Australia
Xiaoyong Du
Renmin University of China, Beijing, China
Joaquim Filipe
Polytechnic Institute of Setúbal, Setúbal, Portugal
Orhun Kara
TÜBİTAK BİLGEM and Middle East Technical University, Ankara, Turkey
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St. Petersburg Institute for Informatics and Automation of the Russian
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Harbin Institute of Technology (HIT), Harbin, China
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Indian Institute of Technology Madras, Chennai, India
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Osaka University, Osaka, Japan
More information about this series athttp://www.springer.com/series/7899
Kangshun Li Jin Li
•

Yong Liu Aniello Castiglione (Eds.)

•

Computational Intelligence
and Intelligent Systems
7th International Symposium, ISICA 2015
Guangzhou, China, November 21–22, 2015
Revised Selected Papers

123
Editors
Kangshun Li Yong Liu
College of Mathematics and Informatics School of Computer Science and
The South China Agricultural University Engineering
Guangzhou The University of Aizu
China Aizu-Wakamatsu, Fukushima
Japan
Jin Li
School of Computer Science Aniello Castiglione
Guangzhou University Department of Informatics
Guangzhou University of Salerno
China Fisciano
Italy

ISSN 1865-0929 ISSN 1865-0937 (electronic)

Communications in Computer and Information Science
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DOI 10.1007/978-981-10-0356-1

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 Preface

The present volume contains the proceedings of the 7th International Symposium on
Intelligence Computation and Applications (ISICA 2015) held in Guangzhou, China,
November 21–22, 2015. ISICA 2015 successfully attracted over 189 submissions.
Through rigorous reviews, 77 high-quality papers were selected for this volume of
Communications in Computer and Information Science (CCIS 575). ISICA confer-
ences are one of the first series of international conferences on computational intelli-
gence that combine elements of learning, adaptation, evolution, and fuzzy logic to
create programs as alternative solutions to artificial intelligence. The past ISICA pro-
ceedings including three volumes of CCIS and four volumes of LNCS have been
accepted in both the Index to Scientific and Technical Proceedings (ISTP) and Engi-
neering Information (EI).
Following the success of the past six ISICA events, ISICA 2015 persisted in
exploring new problems emerging in the fields of computational intelligence. In recent
years, a number of intelligent driving systems for driverless cars have been developed.
For example, at least ten of Google’s self-driving cars, including six Toyota Prius, an
Audi TT, and three Lexus RX450h, have undergone road safety testing. Such
impressive progress makes people think that current techniques have solved all issues
in the design of an intelligent driving system in the sense of overall human perfor-
mance. However, it is simply not the case. There are still many unsolved problems. For
example, Google’s cars are not able to spot a police officer who is waving for traffic to
stop on the side of road. The car’s sensors cannot tell whether a road obstacle is a rock
or a crumpled piece of paper. It is expected that these unsolved problems in such
intelligent systems will become increasingly difficult. While it is difficult to create
intelligence directly, an intelligent system should inherit the simple mechanism of
evolution in which the simple models could produce the evolution of complex
morphologies.
ISICA 2015 featured the most up-to-date research in the analysis and theory of
evolutionary computation, neural network architectures and learning, neuro-dynamics
and neuro-engineering, fuzzy logic and control, collective intelligence and hybrid
systems, deep learning, knowledge discovery, learning, and reasoning. It provided a
venue for fostering technical exchanges, renewing everlasting friendships, and estab-
lishing new connections.
On behalf of the Organizing Committee, we would like to thank warmly the
sponsors, South China Agricultural University, Guangzhou University, Wuhan
University, and China University of Geosciences, who helped in one way or another to
achieve our goals for the conference. We wish to express our appreciation to Springer
for publishing the proceedings of ISICA 2015. We also wish to acknowledge the
dedication and commitment of both the staff at the Springer Beijing office and the CCIS
editorial staff. We would like to thank the authors for submitting their work, as well as
the Program Committee members and reviewers for their enthusiasm, time, and
VI Preface

expertise. The invaluable help of active members of the Organizing Committee,

including Wei Li, Lei Yang, Lixia Zhang, Yan Chen, Lu Xiong, Lei Zuo, Liang Zhong,
Weiguang Chen, and Luyan Guo, in setting up and maintaining the online submission
systems, assigning the papers to the reviewers, and preparing the camera-ready version
of the proceedings is highly appreciated. We would like to thank them for helping to
make ISICA 2015 a success.

November 2015 Kangshun Li

Jin Li
Yong Liu
Aniello Castiglione
 Organization

Program Committee
Shaowei Cai Chinese Academy of Sciences, China
Jiannong Cao The Hong Kong University of Science and
Technology, China
Aniello Castiglione University of Salerno, Italy
Weineng Chen Sun Yat-sen University, China
Yan Chen South China Agricultural University, China
Debiao He Wuhan University, China
Jun He Aberystwyth University, UK
Shuqiang Huang Jinan University, China
Xinyi Huang Fujian Normal University, China
Ying Huang Gannan Normal University, China
Chunfu Jia Nankai University, China
Dazhi Jiang Shantou University, China
Nan Jiang East China Jiao Tong University, China
Jin Li Guangzhou University, China
Kangshun Li South China Agrictural University, China
Ping Li Sun Yat-sen University, China
Wei Li Jiangxi University of Science and Technology, China
Xuan Li Fujian Normal University, China
Zhiqiang Lin Chinese Academy of Sciences, China
Xiaozhang Liu Hainan University, China
Zheli Liu Nankai Universisy, China
Xu Ma Qufu Normal University, China
Wen Sheng Deakin University, Australia
Ke Tang University of Science and Technology of China, China
Ming Tao Dongguan University of Technology, China
Xiang Tao Chongqing University, China
Cong Wang City University of Hong Kong, China
Jiahai Wang Sun Yat-sen University, China
Jianfeng Wang Xidian University, China
Yilei Wang Ludong University, China
Yong Wang Central South University, China
Xianglin Wei Nanjing Telecommunication Technology Research
Institute, China
Di Wu Sun Yat-sen University, China
Lu Xiong Jiangxi University of Science and Technology, China
Honyang Yan Guangzhou University, China
VIII Organization

Lei Yang South China Agricultural University, China

Shuling Yang South China Agricultural University, China
Dongbo Zhang South China University of Technology, China
Lixia Zhang South China Agricultural University, China
Kuo Zhao Jilin University, China
Wei-Shi Zheng Sun Yat-sen University, China
Liang Zhong South China Agricultural University, China

Additional Reviewers

Chen, Peng
Chen, Yan
Li, Wei
Ye, Jun
Zeng, Ling
 Contents

Evolutionary Algorithms

A Hybrid Group Search Optimizer with Opposition-Based Learning

and Differential Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Chengwang Xie, Wenjing Chen, and Weiwei Yu

A New Firefly Algorithm with Local Search for Numerical Optimization . . . . 13

Hui Wang, Wenjun Wang, Hui Sun, Jia Zhao, Hai Zhang, Jin Liu,
and Xinyu Zhou

A New Trend Peak Algorithm with X-ray Image for Wheel Hubs Detection
and Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Wei Li, Kangshun Li, Ying Huang, and Xiaoyang Deng

Community Detection Based on an Improved Genetic Algorithm . . . . . . . . . 32

Kangshun Li and Lu Xiong

Selecting Training Samples from Large-Scale Remote-Sensing Samples

Using an Active Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Yan Guo, Li Ma, Fei Zhu, and Fujiang Liu

Coverage Optimization for Wireless Sensor Networks

by Evolutionary Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Kangshun Li, Zhichao Wen, and Shen Li

Combining Dynamic Constrained Many-Objective Optimization with DE

to Solve Constrained Optimization Problems . . . . . . . . . . . . . . . . . . . . . . . 64
Xi Li, Sanyou Zeng, Liting Zhang, and Guilin Zhang

Executing Time and Cost-Aware Task Scheduling in Hybrid Cloud

Using a Modified DE Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Yuanyuan Fan, Qingzhong Liang, Yunsong Chen, Xuesong Yan,
Chengyu Hu, Hong Yao, Chao Liu, and Deze Zeng

A Novel Differential Evolution Algorithm Based on JADE

for Constrained Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Kangshun Li, Lei Zuo, Wei Li, and Lei Yang

A New Ant Colony Classification Mining Algorithm. . . . . . . . . . . . . . . . . . 95

Lei Yang, Kangshun Li, Wensheng Zhang, Yan Chen, Wei Li,
and Xinghao Bi
X Contents

A Dynamic Search Space Strategy for Swarm Intelligence . . . . . . . . . . . . . . 107

Shuiping Zhang, Wang Bi, and Xuejiao Wang

Adaptive Mutation Opposition-Based Particle Swarm Optimization . . . . . . . . 116

Lanlan Kang, Wenyong Dong, and Kangshun Li

Quick Convergence Algorithm of ACO Based on Convergence

Grads Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Zhongming Yang, Yong Qin, Huang Han, and Yunfu Jia

A New GEP Algorithm and Its Applications in Vegetable Price Forecasting

Modeling Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Lei Yang, Kangshun Li, Wensheng Zhang, and Yaolang Kong

An Optimized Clustering Algorithm Using Improved Gene

Expression Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
Shuling Yang, Kangshun Li, Wei Li, and Weiguang Chen

Predicting Acute Hypotensive Episodes Based on Multi GP . . . . . . . . . . . . . 161

Dazhi Jiang, Bo Hu, and Zhijian Wu

Research on Evolution Mechanism in Different-Structure Module

Redundancy Fault-Tolerant System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
Xiaoyan Yang, Yuanxiang Li, Cheng Fang, Cong Nie, and Fuchuan Ni

Intelligent Simulation Algorithms

Application of Neural Network for Human Actions Recognition . . . . . . . . . . 183

Tomasz Hachaj and Marek R. Ogiela

The Improved Evaluation of Virtual Resources’ Performance Algorithm

Based on Computer Clusters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
Suping Liu

Bayesian Optimization Algorithm Based on Incremental Model Building . . . . 202

Jintao Yao, Yuyan Kong, and Lei Yang

An Improved DBOA Based on Estimation of Model Similarity. . . . . . . . . . . 210

Yuyan Kong, Jintao Yao, and Lei Yang

Person Re-identification Based on Part Feature Specificity . . . . . . . . . . . . . . 219

Dengyi Zhang, Qian Wang, Xiaoping Wu, and Yu Cao

A Gaussian Process Based Method for Antenna Design Optimization . . . . . . 230

Jincheng Zhang, Sanyou Zeng, Yuhong Jiang, and Xi Li
 Contents XI

An Improved Algorithm of Watermark Preprocessing Based on Arnold

Transformation and Chaotic Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Dongbo Zhang and Jingbo Zhang

An Agent-Based Model for Intervention Planning Among Communities

During Epidemic Outbreaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
Loganathan Ponnambalam, A.G. Rekha, and Yashasvi Laxminarayan

The Comparisons Between the Improved Numerical Mode-Matching

Method (NMM) and the Traditional NMM Using for Resistivity Logging . . . 256
Dun Yueqin and Kong Yu

Negative Correlation Learning with Difference Learning . . . . . . . . . . . . . . . 264

Yong Liu

SURF Feature Description of Color Image Based on Gaussian Model . . . . . . 275

Wen Sun, Qian Shen, and Chanjuan Liu

Data Mining and Cloud Computing

An Improved Adaptive Hexagon and Small Diamond Search . . . . . . . . . . . . 287

Fu Mo and Kangshun Li

A Research of Virtual Machine Resource Scheduling Strategy

Based on Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294
Jun Nie

Multiple DAGs Workflow Scheduling Algorithm Based on Reinforcement

Learning in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
Delong Cui, Wende Ke, Zhiping Peng, and Jinglong Zuo

An Improved Parallel K-Means Algorithm Based on Cloud Computing . . . . . 312

Dongbo Zhang and Yanfang Shou

Research on the Integration of Spatial Data Service

Based on Geographic Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
Lei Shang, Shujing Xu, Wei Hou, and Lipeng Zhou

Predicting Maritime Groundings Using Support Vector Data

Description Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
A.G. Rekha, Loganathan Ponnambalam,
and Mohammed Shahid Abdulla

Estimating Parameters of Van Genuchten Equation Based on

Teaching-Learning-Based Optimization Algorithm. . . . . . . . . . . . . . . . . . . . 335
Fahui Gu, Kangshun Li, Lei Yang, and Wei Li
XII Contents

Analysis of Network Management and Monitoring Using Cloud Computing. . . 343

George Suciu, Victor Suciu, Razvan Gheorghe, Ciprian Dobre,
Florin Pop, and Aniello Castiglione

A Game-Theoretic Approach to Network Embedded FEC

over Large-Scale Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353
Christian Esposito, Aniello Castiglione, Francesco Palmieri,
and Massimo Ficco

The Research on Large Scale Data Set Clustering Algorithm

Based on Tag Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365
Qiang Chen

Partitioned Parallelization of MOEA/D for Bi-objective Optimization

on Clusters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373
Yuehong Xie, Weiqin Ying, Yu Wu, Bingshen Wu, Shiyun Chen,
and Weipeng He

A Double Weighted Naive Bayes for Multi-label Classification. . . . . . . . . . . 382

Xuesong Yan, Wei Li, Qinghua Wu, and Victor S. Sheng

An Improved Keyword Search on Big Data Graph

with Graphics Processors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390
Xiu He and Bo Yang

Applications and Security

Rural Micro-credit Decision Model Based on Principle of Risk Control . . . . . 401

Jiali Lin, Dazhi Jiang, and KangShun Li

A Video Deduplication Scheme with Privacy Preservation in IoT . . . . . . . . . 409

Xuan Li, Jie Lin, Jin Li, and Biao Jin

Accurate 3D Reconstruction of Face Image Based on Photometric Stereo. . . . 418

Yongqing Lei, Yujuan Sun, Zeju Wu, and Zengfeng Wang

Business Process Merging Based on Topic Cluster and Process

Structure Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424
Ying Huang and Ilsun You

The BPSO Based Complex Splitting of Context-Aware Recommendation . . . 435

Shuxin Yang, Qiuying Peng, and Le Chen

A Method for Calculating the Similarity of Web Pages Based

on Financial Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445
Lu Xiong, Kangshun Li, and Suping Liu
 Contents XIII

An Expert System for Tractor Fault Diagnosis Based on Ontology

and Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456
Chunyin Wu, Qing Ouyang, Shouhua Yu, Chengjian Deng,
Xiaojuan Mao, and Tiansheng Hong

A Kuramoto Model Based Approach to Extract and Assess

Influence Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464
Marcello Trovati, Aniello Castiglione, Nik Bessis, and Richard Hill

PEMM: A Privacy-Aware Data Aggregation Solution for Mobile

Sensing Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474
Zhenzhen Xie, Liang Hu, Feng Wang, Jin Li, and Kuo Zhao

SmartNV: Smart Network Virtualization Based on SDN . . . . . . . . . . . . . . . 483

Xiaodi Yu, Hu Liang, Fu Tao, Li Jin, and Zhao Kuo

Image Feature Extract and Performance Analysis Based on Slant Transform . . . 489
Jinglong Zuo, Delong Cui, Hui Yu, and Qirui Li

Offline Video Object Retrieval Method Based on Color Features . . . . . . . . . 495

Zhaoquan Cai, Yihui Liang, Hui Hu, and Wei Luo

A Traffic-Congestion Detection Method for Bad Weather

Based on Traffic Video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506
Jieren Cheng, Boyi Liu, and Xiangyan Tang

Uncertainty-Based Sample Optimization Strategies for Large Forest

Samples Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519
Yan Guo, Wenyi Liu, and Fujiang Liu

A PCB Short Circuit Locating Scheme Based on Near Field Magnet

Specific Point Detecting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531
Shuqiang Huang, Jielin Zeng, Hongchun Zhou, Zhusong Liu,
and Yuyu Zhou

Prosodic Features Based Text-dependent Speaker Recognition

with Short Utterance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541
Jianwu Zhang, Jianchao He, Zhendong Wu, and Ping Li

User Oriented Semi-automatic Method of Constructing Domain Ontology . . . 553

Chao Qu, Fagui Liu, Hui Yu, Ruifen Yuan, and Anxiong Wang

Research and Implementation for Rural Medical Information

Extraction Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562
Yutong Gao, Feifan Song, Xiaqing Xie, Shengnan Geng,
and Wenling Tang
XIV Contents

A Finger Vein Recognition Algorithm Using Feature Block Fusion

and Depth Neural Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572
Cheng Chen, Zhendong Wu, Ping Li, Jianwu Zhang, Yani Wang,
and Hailong Li

A New Process Meta-model for Convenient Process Reconfiguration. . . . . . . 584

Xin Li and Chao Fang

Efficient ORAM Based on Binary Tree without Data Overflow

and Evictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 596
Shufeng Li, Minghao Zhao, Han Jiang, Qiuliang Xu, and Xiaochao Wei

A Novel WDM-PON Based on Quantum Key Distribution

FPGA Controller. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608
Yunlu Wang, Hao Wen, Zhihua Jian, and Zhendong Wu

New Security Challenges in the 5G Network . . . . . . . . . . . . . . . . . . . . . . . 619

Seira Hidano, Martin Pečovský, and Shinsaku Kiyomoto

A Method of Network Security Situation Assessment Based on Hidden

Markov Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631
Shuang Xiang, Yanli Lv, Chunhe Xia, Yuanlong Li, and Zhihuan Wang

Chaotic Secure Communication Based on Synchronization Control

of Chaotic Pilot Signal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 640
Honghui Lai and Ying Huang

The Privacy Protection Search of Spam Firewall . . . . . . . . . . . . . . . . . . . . . 648

Kangshun Li and Zhichao Wen

Study on Joint Procurement of Auto Parts Business Partner Selection . . . . . . 659

Bin Liu, Lengxi Wu, Xiaoyan Luo, and Youyuan Wang

Research on Ontology-Based Knowledge Modeling of Design

for Complex Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665
Xiaoyan Luo, Yu Zhou, Bin Liu, and Youyuan Wang

Learning-Based Privacy-Preserving Location Sharing. . . . . . . . . . . . . . . . . . 672

Nan Shen, Xuan Chen, Shuang Liang, Jun Yang, Tong Li,
and Chunfu Jia

A Two-Lane Cellular Automata Traffic Model Under Three-Phase

Traffic Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683
Yu Wang, Jianmin Xu, and Peiqun Lin

Research on Knowledge Association and Reasoning of Product Design . . . . . 689

Nan Jiang, Pingan Pan, Youyuan Wang, and Lu Zhao
 Contents XV

Channel Power Control of Genetic-Nonlinear Algorithm Based on

Impairment Aware in Optical Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . 696
Dongyan Zhao, Shuo Cheng, Yichuan Zheng, Xiaoyu Wang,
and Jian Sun

Optimal Low-Hit-Zone Frequency-Hopping Sequence Set via Cyclotomy. . . . 704

Haiyan Zhao, Xiangqian Dong, Changyuan Wang, and Wenfei Chen

USPD Doubling or Declining in Next Decade Estimated

by WASD Neuronet Using Data as of October 2013 . . . . . . . . . . . . . . . . . . 712
Yunong Zhang, Zhengli Xiao, Dongsheng Guo, Mingzhi Mao,
and Hongzhou Tan

Prediction on Internet Safety Situation of Relevance Vector Machine

about GP-RVM Kernel Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724
Xiaolan Xie, Zhen Long, and Fahui Gu

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735

Evolutionary Algorithms
 A Hybrid Group Search Optimizer
with Opposition-Based Learning
and Differential Evolution

Chengwang Xie(&), Wenjing Chen, and Weiwei Yu

School of Software, East China Jiaotong University, Nanchang 330013, China

chengwangxie@163.com

Abstract. Group search optimizer (GSO) is a recently developed heuristic

inspired by biological group search resources behavior. However, it still has
some defects such as slow convergence speed and poor accuracy of solution. In
order to improve the performance of GSO in solving complex optimization
problems, an opposition-based learning approach (OBL) and a differential
evolution method (DE) are integrated into GSO to form a hybrid GSO. In this
paper, the strategy of OBL is used to enlarge the search region, and the operator
of DE is utilized to enhance local search to improve. Comparison experiments
have demonstrated that our hybrid GSO algorithm performed advantages over
previous GSO and DE approaches in convergence speed and accuracy of
solution.

Keywords: Group search optimizer
Opposition-based learning
Differential

evolution
Hybrid group search optimizer

1 Introduction

In recent years, some swarm intelligence optimization algorithms have emerged, such as
genetic algorithm (GA) [1], simulated annealing algorithm (SA) [2], particle swarm
optimization algorithm (PSO) [3], ant colony algorithm (ACO) [4], differential evolution
algorithm (DE) [5] and so on. These optimization models are all based on group search,
and they have made great progress on the theoretical and applied aspects recently.
In 2006, Sheldon et al. [6] proposed a new algorithm of group intelligence, that is
group search optimization (GSO). The GSO is a novel random group search algorithm
based on producer-scrounger model, simulated the behavior of searching resources for
animals. Some investigations have showed that the GSO has better accuracy and
convergence speed compared to PSO and some other evolutionary algorithms (EAs).
Moreover, the GSO not only employs the simplicity and easy implementation com-
pared to other EAs, but also it uses a particular search model. These characteristics
enable the GSO to avoid some unnecessary structure analysis, thereby saving a lot of
time, and it is especially suitable for the optimization design. However, there exists no
a general optimizer being suitable to solve all kinds of optimization problems due to the
theorem of No Free Lunch. That is to say, optimizers may obtain good performance on
some optimization problems and may behave poor for others.

© Springer Science+Business Media Singapore 2016

K. Li et al. (Eds.): ISICA 2015, CCIS 575, pp. 3–12, 2016.
DOI: 10.1007/978-981-10-0356-1_1
4 C. Xie et al.

The standard GSO is suitable for solving the complex structure design problems
with multimodal, high-dimension and varying dimensions, but it still employs some
shortcomings such as premature convergence and poor accuracy of solution. In order to
improve the GSO, a hybrid GSO with opposition-based learning and differential
evolution (OBDGSO) is proposed in the paper. The opposition-based learning method
travels the current solution space and the opposition-based solution space simultane-
ously, and the better solution is found to view as the new solution, which can increase
the probability of finding the global optimum. In general, DE has a strong ability of
global search at the early stage of the algorithm. The reason is that the early behavior of
mutation in DE can obtain remarkable differences between individuals of the popu-
lation. However, the differences between the individuals become insignificant gradually
at the later stage because the population tends to converge with the evolution process,
correspondingly, the ability of local search of DE become stronger and stronger. So, it
can be found that some advantages of DE, such as fast convergence and not easy to fall
into local optimum, are precisely what the GSO does not have. In this paper, the
OBGGSO on the one hand improves the diversity at the later stage of algorithm, and
enhances the information exchange of the above optimizers (for example, DE, OBL
and GSO), which facilitates the OBDGSO to jump out of the local optimum area and
accelerate the convergence speed.

2 Background

2.1 Group Search Optimizer

Group search optimizer is a new algorithm derived from the nature of social animals
foraging behavior, the process of finding the optimal solution can be viewed as the
behavior of animals foraging. The population of GSO is called group and each indi-
vidual in the population is a member, the number of the individuals in the population is
population size. In GSO, a group consists of three kinds of members: producers,
scroungers and rangers [7]. The behaviors of producers and scroungers follow the
Producer-Scrounger (PS) model, and the rangers perform random walk motions.
In a n-dimensional search space, the ith member at the tth iteration has a current
position Xit 2 Rn , the head angle /ti ¼ ð/ti1 ; . . .; /tiðn1Þ Þ 2 Rn1 and the head direction
Dti ð/ti Þ ¼ ðdi1
t
; . . .; dint Þ 2 Rn can be calculated from /ti via a polar to Cartesian coor-
dinates transformation as follows:
8
> Q
n1
>
> di1 ¼
t
cosð/tip Þ
>
>
>
< p¼1
Q
n1
ð1Þ
> dij ¼ sinð/tiðj1Þ Þ

t
cosð/tip Þ
>
>
>
>






p¼i
>
: t
din ¼ sinð/tiðn1Þ Þ

In GSO, at the kth iteration, the steps of producer Xp behaves as follows:

 A Hybrid Group Search Optimizer 5

(1) the Xp will scan at zero degree and then scan laterally by randomly sampling three
points in the scanning area. The way of calculation for the point at zero degree is
as follow:

Xz ¼ Xpt þ r1 lmax Dtp ð/tp Þ ð2Þ

The point in the right hand side hypercube follows formula (3):

Xr ¼ Xpt þ r1 lmax Dtp ð/tp þ r2 hmax =2Þ ð3Þ

and the point in the left hand side hypercube abides by Eq. (4):

Xl ¼ Xpt þ r1 lmax Dtp ð/tp  r2 hmax =2Þ ð4Þ

where r1 2 R is a normally distributed random number with mean 0 and standard

deviation 1 and r2 2 Rn1 is a random sequence in the range (0,1).
(2) The producer Xp will then find the best point with the best resource (fitness value).
If the better point has a better resource than its current position, then it will move
to this point. Or it will still stay in its current position and turn its head to a new
angle according to Eq. (5).

/pk1 ¼ /kp þ r2 amax ð5Þ

where amax represents the maximum turning angle.

(3) If the producer Xp cannot find a better area after a iterations, it will turn its head
back to zero degree such as formula (6).

/t þ a ¼ /t ð6Þ

where a is a constant.
At each iteration, a number of group members are selected as scroungers. The
scroungers will keep searching for opportunities to join the resources found by the
producer. At the tth iteration, the area copying behavior of the ith scrounger can be
modeled as a random walk towards the producer followed the Eq. (7)

Xit þ 1 ¼ Xit þ r3 ðXpt  Xit Þ ð7Þ

where r3 2 Rn is a uniform random sequence in the range (0,1).

Besides the producer and the scroungers, a small number of rangers have been also
introduced into GSO. In nature, group members often have different searching and
competitive abilities; subordinates, who are less efficient foragers than the dominant
ones, will be dispersed from the group. In GSO, random walks, which are viewed as
the most efficient searching method for randomly distributed resources, are adopted by
the rangers. If the ith group member is selected as a ranger, at the tth iteration, it will
generate a random head angle /i , denoted by formula (8).
6 C. Xie et al.

/it þ 1 ¼ /ti þ r2 amax ð8Þ

where amax is the maximum turning angle; and the ith member will choose a random
distance as Eq. (9)

li ¼ ar1 lmax ð9Þ

and it will move to the new point denoted by formula (10).

Xik þ 1 ¼ Xik þ li
Dki ð/ki þ 1 Þ ð10Þ

In order to improve the probability of finding resources, animals use several

strategies to confine their search to a profitable area, and the important action is to turn
back to the patch where its edge is detected. This strategy is adopted by GSO to handle
the boundary search space. That is to say when a member is outside the search space, it
will turn back to its previous position inside the search space.

2.2 Opposition-Based Learning

As a new model of machine intelligence, the method of opposition-based learning
(OBL) proposed by Rahnamayan et al. [8] has been successfully integrated into many
optimizers. Because OBL can increase the diversity of the population to a certain
extent, then reduce the possibility of falling into local optimum, OBL is often used to
improve the ability of global exploration. Furthermore, Wang et al. [9] presented a
generalized opposition-based learning (GOBL) based on opposition-based learning by
introducing the randomized parameter k, and the approach of GOBL was integrated
into the PSO to design an efficient optimizer. Some basic concepts concerning OBL are
presented as follows.
Definition 1. Let x 2 R be a real number defined on a certain interval: x 2 ½a; b. The
opposite number ~x is defined as follows:

~x ¼ a þ b  x ð11Þ

For a ¼ 0 and b ¼ 1 we receive

~x ¼ 1  x ð12Þ

Analogously, the opposite number in a multidimensional case can be stated as

Definition 2.
Definition 2. Let P ¼ ðx1 ; x2 ; . . .; xn Þ be a point in a n-dimensional coordinate system
~ is completely defined by
with x1 ; x2 ; . . .; xn 2 R, and xi 2 ½ai ; bi . The opposite point P
its coordinates ~x1 ; ~x2 ; . . .; ~xn , where
 A Hybrid Group Search Optimizer 7

~x ¼ ai þ bi  xi ; i ¼ 1; 2; . . .; n ð13Þ

2.3 Differential Evolution

As a global optimizer based on group differences, the differential evolution (DE) has
been widely used in various engineering applications. The advantages of DE can be
stated as follows. (1) DE employs good property of stabilization for some optimization
problems with non-convex, multimodal and non-linear functions. (2) the convergence
speed of DE is faster compared to other optimizers in the same accuracy of solution.
(3) DE is especially suitable to solve multi-variable function optimization problems.
(4) the operation of DE is simple and easy to implement [10].
The main operator of DE is mutation, and DE uses a perturbation of two members
as the vector to add to the third member, which will generate a new vector. This
operation is called mutation. Then, the new vector is mixed with the predefined
parameters according to certain rules to generate trial vector, where the process is called
crossover. If the trial vector of function is inferior to the target function, the test vector
will be replaced in the next generation.
The basic steps of DE [11, 12] are presented as follows.
1) Mutation operation
Based on the generating ways of mutating individuals, we can classify the mutation
into some types. In general, there are three kinds of mutation operators, such as
DE/rand/1/bin, DE/best/1/bin and DE/current-to-best/2/bin. And the first type of
mutation is adopted in this paper.
8
< vi;j ¼ xbest;j þ F  ðxr1;j  xr2;j Þ
vi;j ¼ xbest;j þ F  ðxr1;j  xr2;j Þ ð14Þ
:
vi;j ¼ xi;j þ k  ðxbest;j  xi;j Þ þ F  ðxr1;j  xr2;j Þ

where r1 6¼ r2 6¼ r3 6¼ i, r1 ; r2 ; r3 2 f1; 2; . . .; Ng, and ðxr1;j  xr2;j Þ is differential

vector, F is scaling factor.
2) Crossover operation
In order to improve the diversity of population, the crossover operator is used as
follows. 
vi;j ; ðrandð0; 1Þ  CRÞ or ðj ¼ randð1; DÞÞ
ui;j ¼ ð15Þ
xi;j ; else

where rand(0,1) is a uniform random number within [0,1], and CR is the crossover
constant with outcome 2 [0,1].
3) Selection operation
DE uses a “greedy” selection strategy to ensure the better individual having better
fitness to enter into the next generation. The trial individual will be compared to
parent individuals after the process of mutation and crossover. If the fitness of the
trial individual is better than the parent, it will replace the parent and join into the
next population. Otherwise, the parent remains unchanged and enters into the next
iteration directly.
8 C. Xie et al.

3 Hybrid GSO

A lot of investigations have showed that the shortcomings of GSO, such as poor
convergence speed and easily trapping into local optimum, could attribute to the loss of
diversity of group gradually. So, it is crucial to GSO to improve the diversity to
enhance the efficiency of GSO to solve the complex optimization problems.
As mentioned in Sect. 1, the main merit of DE contains the strong local search
ability, and the method of opposition-based learning is good at global exploration.
Considering the above factors, we combine DE and OBL into GSO to design a hybrid
GSO optimizer.
Let the population size be N, and select 30 percents of the population randomly to
carry out OBL operator to generate a new opposition-based population. Combining the
new population and the original population (whose size is 0.3*N) to sort in descending
order based on the fitness, then the better half of the mixed population would be
selected. Afterwards, the number of 0.4*N of individuals from the rest of the popu-
lation are selected to perform DE operation. At last, the remainder population, whose
size is 0.3*N, will be carried out GSO optimization. The hybrid GSO integrates the
advantages of DE and OBL to better balance the global exploration and local
exploitation to solve the complicated optimization problems effectively.
The flowchart of the hybrid GSO in the paper is presented as follows.
Step 1. Initialize the population P randomly, let the population size be N and the
maximum iteration number be Tmax. Calculate the fitness of each individual, and set
the counter of iteration t = 0.
Step 2. Random select 0.3*N individuals to form a subpopulation SP1, applying
OBL to SP1 to generate an opposition-based population OBP. Combined SP1 and
OBP to select the better half of the mixing population to form subpopulation P1
based on the fitness values.
Step 3. Random select 0.4*N individuals to construct a subpopulation SP2, and
apply DE to SP2 to generate a differential evolution population P2, whose size is
also 0.4*N.
Step 4. Apply GSO to the remainder population whose size is 0.3*N to generate a
population P3, and | P3| = 0.3*N.
Step 5. Combine the subpopulation P1, P2 and P3 to form the next population, and
t = t+1.
Step 6. If t > Tmax, stop; otherwise, goto Step 2.

4 Experimental Results and Analysis

In order to test the validity of the hybrid GSO, we select two representative optimizers
as peer comparison algorithms, the one is GSO proposed by He S [13]. in 2009, and the
other is the original DE proposed by Storn et al. The comparable experiments in the
paper are all based on 13 benchmark single objective optimization problems [14]. And
the 13 test problems are listed in Table 1. These 13 benchmark functions can be classify
into three kinds as follows. (1) unimodal functions. (2) simple multi-peak functions.
 A Hybrid Group Search Optimizer 9

Table 1. The 13 classical test functions

Function Name Feasible solution space fmin
f1 Sphere [−100,100] 0
f2 Schwefel’s Problem 2.22 [−10,10] 0
f3 Schwefel’s Problem 1.2 [−100,100] 0
f4 Schwefel’s Problem 2.21 [−100,100] 0
f5 Rosenbrock [−30,30] 0
f6 Step [−5.12,5.12] 0
f7 Quartic [−1.28,1.28] 0
f8 Schwefel’s Problem 2.6 [−500,500] 0
f9 Rastrigin [−5.12,5.12] 0
f10 Ackley [−32,32] 0
f11 Griewank [−600,600] 0
f12 Penalized 1 [−50,50] 0
f13 Penalized 2 [−50,50] 0

Table 2. The average results for each algorithm on each test function
Function GSO DE OBDGSO fmin
f1 21.2789 0.1803 9.12E-07 0
f2 0.1779 1.0614 7.20E-08 0
f3 4.63E+04 2.19E+03 1.41E+04 0
f4 73.1417 4.5009 62.4728 0
f5’ 4.48E+07 6.14E+06 3.61E+06 0
f6’ 1.31E+03 77.4618 22.3586 0
f7 3.23E-08 1.40E-06 1.49E-11 0
f8 3.91E+03 7.64E+03 3.71E+03 0
f9 1.05E+02 2.18E+02 18.3557 0
f10 11.4299 10.7654 6.0519 0
f11 1.2249 0.3278 3.08E-06 0
f12’ 1.79E+06 6.1801 4.2264 0
f13’ 3.90E+06 18.4162 8.4528 0

The above two types of functions are mainly used to test the optimization accuracy.
(3) non-rotating multi-peak functions. This kind of functions have many local extreme
points, which is hard for the average optimizers to find the global optimum. So, the
third type of test functions are often used to test the global optimization performance
and the ability of avoiding premature convergence.
All experiments in the paper are based on Windows 7 operating system, dual-core
2.50 GHz Intel processor and 4G memory, and Matlab 2010 programming platform.
In order to compare the performances of peer algorithms, we set some identical
running parameters, such as the group (population) size is 100, the dimension of
decision variable is 30, and the maximum number of iterations is set to 1000.
10 C. Xie et al.

Table 3. Statistical mean and standard deviation of obtained by OBDGSO, GSO and DE on 13
test functions over 30 independent runs
Test OBDGSO GSO DE
instance
f1 Mean 1.00E-09 8.1584 0.1432
Std. 1.70786E-06 18.99110904 0.032472599
t-test + +
f2 Mean 7.40E-09 0.0408 0.8419
Std. 4.39996E-08 0.118550676 0.211006374
t-test + +
f3 Mean 9.44E+03 2.96E+04 1.61E+03
Std. 3997.819643 10703.80471 671.7363074
t-test + =
f4 Mean 51.6286 61.9726 3.9978
Std. 8.146587239 7.455841639 0.349443413
t-test + =
f5 Mean 9.41E+05 3.04E+07 5.21E+06
Std. 3270285.171 14123670.35 900927.2766
t-test + +
f6 Mean 5.7405 3.85E+02 50.5079
Std. 11.23317659 964.833203 19.58850474
t-test + +
f7 Mean 8.93E-11 1.00E-10 8.00E-09
Std. 4.36417E-12 5.54543E-08 2.56192E-06
t-test + +
f8 Mean 3.08E+03 3.33E+03 7.33E+03
Std. 387.9434117 387.4388644 555.615874
t-test = +
f9 Mean 0.0029 6.15E+01 2.10E+02
Std. 24.75970942 47.18212305 6.143087229
t-test = =
f10 Mean 2.9365 10.2191 10.3556
Std. 2.376233926 0.664516162 0.203911203
t-test = =
f11 Mean 1.60E-07 1.0465 0.2912
Std. 4.22386E-06 0.229185544 0.066716038
t-test + +
f12’ Mean 2.2658 2.42E+05 5.3495
Std. 1.654364408 1861291.275 1.137839227
t-test + =
f13’ Mean 1.6379 6.09E+05 10.8051
Std. 7.899360368 3198753.277 5.047741332
(Continued)
 A Hybrid Group Search Optimizer 11

Table 3. (Continued)
Test OBDGSO GSO DE
instance
t-test + =
Better(+) 10 7
Same(=) 3 6
Worse(−) 0 0
Score 10 7

In addition, the algorithmic parameters of GSO are used followed [15], and the
algorithmic parameters of DE are followed [16] and [17].
Our OBDGSO is compared with the other two optimizers, such as GSO and DE, all
three algorithms are carried out 30 times repeatedly, and we can obtain the statistical
average data as the experimental results, which are listed in Table 2. We can observe
that the OBDGSO has the best accuracy of solution in all 13 test functions among the
three peer algorithms.
Table 3 lists the statistical mean and standard deviation results obtained by
OBDGSO, GSO and DE on 13 test functions over 30 independent runs. It can be seen
that the OBDGSO has significant performance advantages over GSO and DE. So we
can conclude that OBDGSO is promising optimizer in solving multi-modal,
high-dimensional functions.

5 Conclusion

The paper proposed a hybrid GSO with opposition-based learning and differential
evolution, called OBDGSO. The hybrid GSO utilizes the method of opposition-based
learning to enhance the ability of global exploration and uses differential evolution to
improve the local search ability. Three peer comparison algorithms (GSO, DE and
OBDGSO) are performed to comparing experiments on 13test functions, the experi-
mental results show that OBDGSO has significant advantages over GSO and DE in
accuracy of solution and convergence speed. So, the conclusion is that the OBDGSO in
the paper is a promising optimizer in solving multi-modal, high-dimensional functions.

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 A New Firefly Algorithm with Local Search
for Numerical Optimization

Hui Wang1,2(&), Wenjun Wang3, Hui Sun1,2, Jia Zhao1,2,

Hai Zhang1,2, Jin Liu4, and Xinyu Zhou5
1
School of Information Engineering, Nanchang Institute of Technology,
Nanchang 330099, China
huiwang@whu.edu.cn
2
Jiangxi Province Key Laboratory of Water Information Cooperative Sensing
and Intelligent Processing, Nanchang 330099, China
3
School of Business Administration, Nanchang Institute of Technology,
Nanchang 330099, China
4
School of Computer, Wuhan University, Wuhan 430072, China
5
College of Computer and Information Engineering, Jiangxi Normal University,
Nanchang 330022, China

Abstract. Firefly algorithm (FA) is a recently proposed swarm intelligence

optimization technique, which has shown good performance on many opti-
mization problems. In the standard FA and its most variants, a firefly moves to
other brighter fireflies. If the current firefly is brighter than another one, the
current one will not be conducted any search. In this paper, we propose a new
firefly algorithm (called NFA) to address this issue. In NFA, brighter fireflies
can move to other positions based on local search. To verify the performance of
NFA, thirteen classical benchmark functions are tested. Experimental results
show that our NFA outperforms the standard FA and two other modified FAs.

Keywords: Firefly algorithm (FA)
Swarm intelligence
Local search

Numerical optimization

1 Introduction

Firefly algorithm (FA) is a new swarm intelligence algorithm developed by Yang in

2010 [1]. It is inspired by the social behavior of fireflies based on the flashing and
attraction characteristics of fireflies. In the past five years, the research of FA has
attracted much attention. Different versions of FA has been designed to solve bench-
mark or real-world optimization problems [2–6].
To enhance the performance of FA, Farahani et al. [7] proposed a Gaussian dis-
tributed FA (GDFA). Computational results on five benchmark functions show that
GDFA outperforms PSO and the standard FA. Tilahun and Ong [8] modified the
random movement of the brighter firefly by generating random directions in order to
determine the best direction. If such a direction is not generated, it will remain its
current position. Moreover, the assignment of attractiveness is modified in such a way
that the effect of the objective function is magnified. Simulation results show that the

© Springer Science+Business Media Singapore 2016

K. Li et al. (Eds.): ISICA 2015, CCIS 575, pp. 13–22, 2016.
DOI: 10.1007/978-981-10-0356-1_2
14 H. Wang et al.

modified FA performs better than the standard FA in finding the best solution with
smaller CPU time. Fister et al. [9] proposed a memetic FA (MFA) to solve combi-
natorial optimization problems. In MFA, the parameter a is dynamically adjusted, and
the parameter b is changed in the range [0.2, 1.0] based on the distance between two
fireflies. Additionally, the random part ae for the movement of the attraction is scaled
by the size of the search range. Experimental results show that the MFA is significantly
better than the standard FA. In our previous work [10], the MFA is used as the standard
FA and combined with other strategies. Gandomi et al. [11] introduced chaos into FA
to increase its global search ability for robust global optimization. Different chaotic
maps are utilized to tune the attractive movement of fireflies. Results show that the
chaotic FA (CFA) outperforms the standard FA. In [12], quaternion is used for the
representation of individuals in FA so as to enhance the performance of the firefly
algorithm and to avoid any stagnation. Yu et al. [13] designed a new FA with a wise
step strategy (WSSFA), which considers the information of firefly’s personal and the
global best positions. Results show that the modified algorithm outperforms the stan-
dard FA on twenty benchmark functions. In [14], a variable step size FA (VSSFA) is
proposed, where a dynamical method is used to update the parameter a. Computational
results show that WSSFA and VSSFA achieve better solutions than the standard FA on
a set of low-dimensional benchmark functions (D = 2). However, our experiments
demonstrate that both of them can hardly obtain reasonable solutions for some
high-dimensional problems (D = 30). Compared to WSSFA and VSSFA, MFA can
achieve promising solutions.
In the FA, the fitness function for a given problem is associated with the light
intensity. The brighter the firefly is, the better the firefly is. That means a brighter firefly
has a better fitness value. The search process of FA depends on the attractions between
fireflies. Based on these attractions, a firefly tends to move other brighter fireflies. If a
firefly is brighter than another one, the brighter firefly will not be conducted any search.
In this paper, we propose a new FA (called NFA) to avoid this case. When the current
firefly is brighter than another one, a local search operation is conducted on the current
one to provide more chances of finding more accurate solutions. It is noted that the
proposed NFA is implemented based on the MFA. Therefore, the NFA is a hybrid
algorithm by combining the MFA and the proposed local strategy. To verify the
performance of NFA, a set of well-known benchmark function with D = 30 are tested.
Experimental results show that NFA performs better than the standard FA, MFA, and
VSSFA.
The rest paper is organized as follows. In Sect. 2, the standard FA is briefly
introduced. In Sect. 3, the proposed NFA is described. Experimental results are pre-
sented in Sect. 4. Finally, the work is concluded in Sect. 5.

2 Firefly Algorithm

As mentioned before, the FA mimics the behavior of the social behavior of the flashing
characteristics of fireflies. To simply the behavior of fireflies and construct the search
mode of FA, three rules are used as follows [1]:
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 peptic ferment in, 16
Foam structure, 6
Folliculina, 137;
tube of, 152
Food, 35 f. (see also Ingestion);
of Higher Animals, 38;
absorption of, by Plants, 38;
in relation to life-cycle of Ciliata, 147 f.;
of Sponges, 237;
of Hydra, 256 and n.;
of Millepora, 261;
of Siphonophora, 304;
of Charybdea, 319;
of Alcyonium, 339;
of Zoantharia, 373;
of Asterias rubens, 439;
of Ophiothrix fragilis, 486;
of Ophiolepididae, 496;
of Echinus esculentus, 516;
of Echinarachnius parma, 546;
of Echinocardium cordatum, 552;
of Holothuria nigra, 561;
of Dendrochirota, 572;
of Synapta inhaerens, 577;
of Antedon rosacea, 583
Food-vacuole, of Actinosphaerium eichornii, 72;
of Ciliata, 145 f.;
of Carchesium, 146
Foot-plate, of young Pentacrinidae, 592;
of larva of Antedon rosacea, 619
Foraminifera, 40, 49, 50, 58 f.;
relations of, 49;
shell of, 49, 59 f., 60, 61, 63, 65;
habitat of, 59 f.;
literature of, 58 n.;
marine, 60 f.;
nuclei of, 62, 67 f.;
 nutrition of, 40, 62;
streaming of granules in, 17;
chromidia of, 62, 68 f.;
collection of, 62;
dimorphism of, 66, 67;
reproduction of, 67 f.;
economic uses of, 69 f.;
palaeontology of, 69 f.
Forbes, 338
Force, dual, of dividing-cell, 26 n.
Forcepia, 223
Forceps, 222
Forcipulata, 462, 473 f.
Forcipulate pedicellaria, 456, 473
Formative vacuole of contractile vacuole, in Flagellata, 110, 115;
in Ciliata, 143
Fossil, Foraminifera, 69 f.;
Radiolaria, 87 f.;
Dinoflagellata, 132;
Peridinium, 132;
Sponges, 192 f., 207 f., 215, 241;
Coelenterates, 270, 281 f., 343 f., 346, 393 f., 406;
Asteroidea, 475 f.;
Ophiuroidea, 501 f.;
Echinoidea, 556 f.;
Crinoidea, 594 f.;
Thecoidea, 596;
Carpoidea, 596 f.;
Cystoidea, 597 f.;
Blastoidea, 599 f.
Fowler, 293 n., 382, 400, 404
Framboesia, 121 n.
France, epidemic of pébrine in, 107
Francé, on structure of funnel of Choanoflagellates, 115 n., 121 n.;
monograph of Choanoflagellates, 123, 182 n.;
on Polytomeae, 119 n.
Freetown, prophylaxis of malaria at, 106
Fringing reef, 390 f.
Frog's blood, Lankesterella in, 102
Frondicularia, 59, 63
Fructification, of Mycetozoa, 90 f.;
of Acrasieae, 90;
of Myxomycetes, 49, 91 f.
Fry, E. and A., on Myxomycetes, 93 n.
Fuligo, 90;
F. varians, 92 f.;
pepsin in, 16
Fungacea, 402
Fungi, cell connexions in, 37 f.;
in relation to Protista, 40;
Gasteromycetous, 91
Fungia, 403;
asexual reproduction of, 388, 389;
F. crassitentaculata, 403
Fungiidae, 403
Funiculina, 359, 362;
F. quadrangularis, 362
Funiculinidae, 362
Funnel, of Craspedomonadidae or Choanoflagellates, 111, 121, 122,
182;
of Phalausteridae, 111;
of choanocytes of Sponges, 171
Fusion of larval Sponges, 174
Fusion-nucleus of Ciliata, 150
—see also Reproduction, Syngamy, Zygotonucleus
Fusulina, 59

Galaxea, 400;
G. esperi, 400
Galeolaria, 307;
G. biloba, 304
Galeolarinae, 307
Galerites, 558
Gamble, 312 n.;
 and Keeble, 175 n.
Gametes, 33 f.;
of Trichosphaerium, 54;
of certain Protomastigaceae, 116 n.;
of Volvocidae, 127 f.;
of Pandorina (of three sizes), 128, 129
Gametocyte of Acystosporidae, 104 f.
Gametogonium (= parent-cell of gametes), male, of Acystosporidae,
105
Gametonuclei (= nuclei capable of syngamous fusion), 34
Ganeria, 464
Ganeriidae, 454, 464
Gardiner, 345, 370, 375, 392 n., 404
Garveia, 270
Gasteromycetous fungi, 91
Gastral layer, 171
Gastralia, 201
Gastropores, 257, 258
Gastrozooids, of Millepora, 259, 260;
of Hydractinia, 264;
of Siphonophora, 299;
of Antipatharia, 408
Gastrula, definition of, 603
Gaule, misinterpretation of nature of Haemosporidae, 102
Gegenbaur, 302
Gellius 217, 223;
G. varius, development, 172 f., 173, 174
Gemmantes, 400
Gemmaria, 405
Gemination = Budding, q.v.
Gemmiform, pedicellariae, of Echinus esculentus, 506;
of E. acutus, 509;
of E. elegans, 510;
of Cidaridae, 534;
of Echinarachnius parma, 544;
of Echinocardium cordatum, 550
Gemmule, 177, 178, 179, 230
Generation, spontaneous, 42 f.
Generations, alternation of, 44, 250
Genital base of Holothuria nigra, 567
Genital bursa, of Ophiothrix fragilis, 485
compared with hydrospires of Blastoidea, 600
Genital canal of Antedon rosacea, 586
Genital organs (including ducts), of Asterias rubens, 451 f.;
of Ophiothrix fragilis, 490;
of Ophiarachna, 491;
of Ophiuroidea, 494;
of Amphiura squamata, 494;
of Echinus esculentus, 528;
of Echinocardium cordatum, 552;
of Hemiaster philippi, 552;
of Holothuria nigra, 567;
of Antedon rosacea, 586
Genital plate, of Ophiothrix fragilis, 485;
of Echinus esculentus, 512, 513
Genital rachis, of Asterias rubens, 452;
of Ophiothrix fragilis, 490;
of Echinus esculentus, 528;
of Antedon rosacea, 586
Genital scale of Ophiothrix fragilis, 485
Genital stolon, of Asterias rubens, 451;
of Ophiothrix fragilis, 489;
of Echinus esculentus, 528;
of Holothuria nigra, 567;
of Antedon rosacea, 585, 586;
of larva of A. rosacea, 619
Geodia, 211
Geographical distribution of Protozoa, 47
Geotaxy (= barotaxy), 20
Gephyra dohrnii, 382, 408
Gephyrea, 577
Gerarde, 167
Gerardia savalia, 406
Gerbillus indicus infested by a Haemosporidian, 102 n.
Germinal spot (= nucleole of ovum), 7
Germinal vesicle (= nucleus of ovum), 7
Germination, 32;
of Myxosporidian spores, 107
Germ-plasm, 28 f.;
continuity of, 172
Germ theory, 44
Germs, invisible air-borne, 43
Geryonia, 290, 295
Gilchrist, 338
Gill of Echinus, 514, 527
Gill-cleft, of Echinus, 514;
of Sphaerechinus, 540 f.;
of Strongylocentrotus, 541
Ginkgo, spermatozoa of, 38
Glaucoma, 137, 153;
G. scintillans, rate of fission of, 147 f.
Glauconite, 70
Globiceps, 272
Globiferae of Centrostephanus longispinosus, 532
Globigerina, 59, 63, 242;
spines of, 61, 66;
-ooze, 61 f.;
G. bulloides, 68, 69
Glossina morsitans, intermediate host of Trypanosoma brucei, 119;
G. palpalis, intermediate host of T. gambiense, 120
Glossograptus, 282
Glycerin, 15
Glycogen, of Ciliata, 144;
-vesicles of Pelomyxa palustris, 53
Gnat (Anopheles), intermediate host of Haemamoeba and
Laverania, 103 f.;
(Culex) intermediate host of Haemoproteus, 103;
of Trypanosoma, 120
Golgi, on relation of Acystosporidian life-cycle and stages of
intermittent fever, 103
Gonactinia, 371, 372, 377
Gonangium, 276
Goniaster, 471;
fossil, 475
Goniastraea, 375, 401
Goniocidaris, 534;
G. canaliculata, 535
Gonionema, 288, 291;
G. murbachii, 232, 290, 291, 292
Gonium, 111
Gonophore, of Gymnoblastea, 265;
of Calyptoblastea, 277;
of Stylasterina, 284;
of Siphonophora, 302
Gonotheca, 276, 281
Gonozooids, of Siphonophora, 302;
of Antipatharia, 408
Gorgonacea, 350 f.
Gorgonella, 357;
spicule, 336
Gorgonellidae, 337, 357
Gorgonia, 356;
G. cavolinii, 340;
G. flabellum, 357;
G. verrucosa, 356
Gorgoniidae, 334, 337, 356
Gorgonocephalus, 491, 501
Gosse, 273
Goto, 291, 293;
on development of Bipinnaria, 612
Grammaria, 278
Granatocrinus, 599;
G. norwoodi, 600
Grant, 167
Grantiidae, 192
Grantiopsis, 191
Granular disintegration of Protista, 14 f.
Granules, in protoplasm, 6;
 excretory, 6, 144;
aleurone, 37;
basal, of cilia, etc., 138 n., 141 (see also Blepharoplast);
proteid, of Suctoria, 161
Graphiohexaster, 203
Graptolitoidea, 281
Grassi, on malarial parasites, 103
Gravity, stimulus of, 19 f.
Greasy film, outer clear layer of protoplasm behaves like, 17
Greeff, on Protozoa, 46
Green Flagellates, relations of, 48
Greensand, 70;
Cambridge, 208
Green water often due to Euglena viridis, 124
Greenwood, M., on peptic digestion in Protozoa, 16;
on feeding of Carchesium polypinum, 45 f., 146 f.
Gregarina, 97, 98, 99;
G. blattarum, 98
Gregarines, habitat, 99;
syngamy, 99
Gregarinidaceae, 95 f., 97 f.
Gregory, 346
Grew, 166
Grey chalk, 61
Gromia, 52;
G. oviformis, 59 n.
—see also Allogromia
Grooves, longitudinal and transverse, of Dinoflagellata, 110, 130,
131, 132;
of Peridinium, 131;
of Polykrikos, 132;
oral, of Noctiluca, 133
Grosvenor, 249 n.
Growth, 19 f.;
Spencer's limit of, 23, 31
Gruber, on regeneration in Protozoa, 35 n.;
on diffused nucleus in marine Ciliata, 144 n.;
 on tubicolous marine Ciliata, 152
Gruppe, deposit of Radiolaria, 87
Guinea Coast, 106
Gullet (= pharynx) of Paramecium caudatum, 151
Gut, supposed, of Ciliata, 145
—see also Alimentary canal
Gutter, oral, of Vorticellidae, 156, 158
Gymnamoebae, 51 n.
Gymnasteridae, 471
Gymnoblastea, 262 f.
Gymnodinium, 110;
G. pulvisculus, parasitic in Appendicularia, 132
Gymnomyxa, 49 n.
Gymnophrys, 58
Gymnosphaera, 70, 73
Gymnostomaceae, 137;
predaceous, trichocysts of, 143;
mouth and pharynx of, 145;
noteworthy members of, 152
Gyractis, 380

Häcker, on skeleton of Radiolaria, 82 n.

Haddon, 382
Haeckel, 168, 185, 192, 237, 308;
on Monera, 4 n., 89 n.;
on Protista, 40 f.;
on Protozoa, 46;
on Heliozoa, 71;
on classification of Radiolaria, 76;
on functions of porocone in Radiolaria, 81;
on enumeration of Radiolaria, 87 f.;
on Myxobrachia, 83;
on Sponges, 168, 192;
on phylogeny of Echinodermata, 622
Haeckeliana, 79, 85
Haemamoeba, 97, 103 f.;
H. malariae, parasite of quartan fever, 104 f.;
 H. vivax, parasite of tertian fever, 104 f.
Haematochrome, 125
Haematococcus (= Sphaerella, 111), 125, 126
Haemoflagellates (= Trypanosoma, etc., q.v.), 119 n.
Haemoglobin, 103;
in water-vascular system of Ophiactis virens, 499
Haemogregarina, 97
Haemomenas (Ross's name for parasite of pernicious fever =
Laverania, 97), 105
Haemoproteus, 97;
parasitic in birds, 103
Haemosporidae, 97, 102
Haimea hyalina, 342;
H. funebris, 342.
Haimeidae, 342
Halcampa, 366, 380;
H. chrysanthellum, 380
Halcampidae, 375, 380
Halecium, 277, 280
Haleremita, 256
Halicalyx, 291
Halichondria, 217, 223;
H. panicea, structure, etc., 168 f., 169, 170, 211
Halichondrina, 216, 217
Haliclystus, 320, 321;
H. auricula, 320
Halicnemia, 216, 224
Haliomma, 77
Haliphysema, 59
Halisarca, 196, 225
Halomitra, 404
Halteria, 137, 152, 155
Halteridium, 97;
sexual fusion in, 103, 105;
regarded by Schaudinn as a state of Trypanosoma, 103 n., 120
Hamacantha, 223
Hamann, on supposed cavities in the body-wall of Asteroidea, 449;
 on classification of Zygophiurae, 495 n.
Hanitsch, 168 n.
Hapalocarcinus, 402
Hardy, on structure of protoplasm and clearing, 11, 12 n.
Hartea elegans, 342
Hartlaub, 269 n., 274, 297 n.
Hartog, on Protozoa, 1 f.;
on structure of protoplasm, living and dead, 11;
on function of contractile vacuole, 15 n.;
on intracellular digestion, 16;
on brood-division (multiple cell-division), 16 n.;
on dual force of dividing cell, 26 n.;
on syngamy, etc., 34 n.;
and Dixon, on pepsin in Pelomyxa, 16
Harvey, "omne vivum ex ovo," 42
Hastigerina, 59, 66
Hauerina, 59
Heart, of Asterias rubens, 450
Heart-urchins = Spatangoidea, q.v.
Heat—see Temperature
Heat-rigor, 22
Heleopera, 52;
test of, 55
Heliaster, 474
Heliasteridae, 453, 454, 474
Heliolites, 346
Heliolitidae, 346
Heliopora, 330, 334, 337, 345 f.
Helioporidae, 346
Heliozoa, 50, 70 f.;
streaming of granules, 17;
regeneration, 35;
habitat, 48;
locomotion, 73;
various forms of, 74;
marine, 75;
distribution of, 75;
 resemblance of Suctoria to, 159
Hemiaster, 556;
H. philippi, 552, 555, 602, 603
Hemichordata, 616
Henneguy, on protoplasm, 3 n.;
on syngamy, 34 n.
Henneguya, 98
Henricia—see Cribrella
Hérouard, Delage and, on Protozoa, 46
Herpetolitha, 404
Herpetomonas, 115
Hertwig, R., on Protozoa, 46;
on chromidia in Sarcodina, 52 n.;
on Heliozoa, 71;
on Radiolaria, 88;
on Suctoria, 161 n., 162
Heteractinellida, 208
Heterastridium, 283
Heterocentrotus, 532, 542
Heterocoela, 187 f.
Heterophrys, 71
Heteropidae, 192
Heterotrichaceae, 137, 153 f.;
fission of, 147
Heteroxenia, 333, 335, 348
Hexactine (a triaxon in which all six actines are developed), 184
Hexactinellida, 194, 195, 197 f., 228, 240
Hexadella, 196
Hexamitus, 115
Hexaster (a hexactine with secondary or terminal rays = Carter's
"rosette"), 203
Hexasterophora, 203 f.
Hickson, on interchange of cytoplasm in conjugating Infusoria, 149
n.;
on conjugation in Suctoria, 161 f.;
on Coelenterata, 243 f.;
on Millepora, 259 n.;
 on Stylasterina, 286 n.;
on Alcyonaria, 329 n., 351 n., 352 n., 359 n.;
on Antipatharia, 408 n.;
on Ctenophora, 412 f.
Hieronymus on Chlamydomyxa, 90 n.
Hincks, 268 n.
Hinde, 193, 207
Hippasterias, 471
Hippopodius, 307
Hippospongia, 221
Holectypoidea, 558
Holophytic, Algae and Fungi, zoospores of, 5;
nutrition, 37;
Flagellates, 113
Holopodidae, 592
Holopus, 588, 589, 594
Holothuria, 570;
H. nigra, 561 f.;
shape, 561;
feelers, 561;
body-wall, 562;
alimentary canal, 562;
respiratory trees, 563;
water-vascular system, 564;
nervous system, 566;
calcareous ring, 566;
blood system, 567;
genital organs, 567;
H. cinerascens, 567;
H. fusco-rubra, 567;
H. aspera, 570;
H. intestinalis, 570;
H. tremula, 570
Holothuroidea, 431, 537, 560 f., 583;
mesenchyme of larva, 604;
development of, 609, 614, 615;
phylogeny of, 622
Holotrypasta (= Porulosa), 76
Holozoic, 35 f.;
Flagellates, 113;
Dinoflagellates, 131
Holt, 311 n.;
on burrowing habits of Strongylocentrotus lividus, 541 n.
Homaxonic (= symmetrical about a centre along an indefinite
number of equivalent axes), 76
Homocoela, 185 f.
Homoeonema, 294
Homostichanthus anemone, 383
Honey-bees, alleged spontaneous generation of, 42
Hormiphora, 418;
H. plumosa, 413
Human diseases, produced by Coccidiaceae, 102 f.;
by Amoeba histolytica, 57;
by Trypanosomids, 119 f.
Huxley, on Protozoa, 45;
first description of a living Radiolarian, 88;
on Cystoflagellates, 135
Hyalonema, 203, 204;
H. sieboldi, 206;
H. thomsoni, 204, 221
Hyalopus, 52;
H. dujardini, 59 n.
Hyalosphenia, 52;
H. lata, 55
Hyboclypus, 558;
H. gibberulus, 558
Hybocodon (Corymorphidae, 273), 265
Hydatina senta, often found with Euglena viridis, 124
Hydra, 253 f., 255;
nematocysts of, 247;
species of, 256;
specific gravity of, 13 n.;
host of Kerona and Trichodina, 158;
Zoochlorella in, 126, 256;
 H. oligactis (= fusca), 253, 256;
H. pallida, 256 n.;
H. viridis, 253, 256;
H. vulgaris, (= grisea), 253, 256;
nematocyst, 247
Hydractinia, 263, 265, 268, 270
Hydrallmania falcata, 278
Hydrichthys mirus, 268
Hydroceratinidae, 279
Hydrocladia, 276
Hydrocoel (including left hydrocoel), 428;
of Antedon rosacea, 585;
development of, 608 f., 609;
development in Asterina gibbosa, 611;
in Auricularia, 615;
in Antedon rosacea, 619
Hydroctena salenskii, 423, 424
Hydrolaridae, 273
Hydrophyllium, 297, 300
Hydrorhiza, 262
Hydrosome, 250, 251
Hydrospire, of Blastoidea, 580, 599;
of Codaster, 599;
of Pentremites, 599;
of Granatocrinus, 599 f.
Hydrotheca, 275
Hydrozoa, 249 f.;
hydrosome, 250;
life-history, 250;
medusome, 250, 251 f.;
sense-organs, 252
Hydrurus, 110;
theca of, 113
Hymedesmia, 222
Hymenaster, 466;
H. pellucidus, 465
Hymeniacidon, 224
Hymeraphia, 223
Hyocrinidae, 590
Hyocrinus, 588, 589, 590, 590;
H. bethellianus, 590
Hyperia (Amphipod), parasitic in Radiolaria, 87
Hyphalaster, 471;
H. moseri, 459
Hypnocyst, of Rhizopoda, 57;
of Proteomyxa, 88 f.;
of Pseudospora, 89;
of Myxomycetes, 90 f.
Hypolytus peregrinus, 262, 271 n.
Hypophare, 210
Hypostome, 250
Hypotrichaceae, 137, 138 f., 158 n.

Ianthella, 220
Ichthyophtheirius, 137;
noxious parasite of fish, 152
Iciligorgia, 351
Idioplasm, 29
Ijima, 199, 206, 231, 234
Ileonema, 137, 152
Ilyanthus mitchellii, 380
Ilyodaemon, 571, 572;
I. maculatus, 571
Imperforate, Foraminifera, 58 f.;
Corals, 371
Inadunata, 595
Incurrent canal, 170
India, diseases of Trypanosomic origin, 119 f.
Induction shocks, action on Protozoa, 7, 22
Infero-marginal ossicle of Asteroidea, 436
Inflammation, 8
Infra-basal plate, of Crinoidea, 588;
of fossil Crinoidea, 594;
of larval Antedon rosacea, 619
Infundibulum, 415
Infusions, appearance of organisms in, 42 f.;
organisms of, 136
Infusoria, 40, 48, 50, 136 f.;
specific gravity of, 13 n.;
zygote does not encyst, 34.
Ingestion, of food, by Amoeba limax, 9;
by Choanoflagellates, 122;
by Dinoflagellates, 131;
by Carchesium, 146;
by Coleps, 150
—vacuole of, in Flagellates, 113;
in Oikomonas, 112;
in Choanoflagellates, 122
Inner perihaemal ring-canal, of Asterias rubens, 448;
development of, in Asterina gibbosa, 612
Inoculation of malarial fever in man through a mosquito, 105 f.
Insectivorous plants, 38
Insects, metamorphoses of, 44;
as hosts of Trichonymphidae, 123
Interambulacral area, of Echinarachnius parma, 544;
of Echinocardium cordatum, 550
Interambulacral plate, of Echinus esculentus, 511;
of Cidaridae, 533 f.;
of Echinarachnius parma, 544 f.
Interbrachial septa—see Interradial septa
Interchanges between cell and medium, 14
Intermediate dorsal process of ciliated band of Auricularia, 608
Intermediate (= supplemental) skeleton of Perforate Foraminiferal
shell, 63, 66
Intermittent fever, malarial, produced by Acystosporidae, 103 f.
Internal budding of Suctoria, 160 f., 162;
of Ephydatia, 177
Internal gills—see Stewart's organs
Internal movements of protoplasm, 17
Interradial plates, of calcareous ring of Holothuria nigra, 566;
of Holothuroidea, 569;
 of Synaptida, 569;
of Dendrochirota, 569;
of calyx of Crinoidea, 589;
of Thaumatocrinus, 589;
of Hyocrinus, 590;
of Rhizocrinidae, 591;
of corona of Echinoidea—see Interambulacral plate
Interradial septa, of Asterias rubens, 437;
of Heliasteridae, 474;
absent in Brisingidae, 475
Interradius, 428;
of Asterias rubens, 434;
of Echinus esculentus, 504;
of Holothuria nigra, 562
Interstitial growth, 10
Intestine, 415;
of Echinus esculentus, 516;
of Holothuria nigra, 563;
of Antedon rosacea, 583;
of Actinometra, 589;
of Dipleurula, 605;
of Protocoelomata, 616
Intracapsular protoplasm of Radiolaria, 80 f.
Intramolecular respiration, 14 n.
Intranuclear spindle of Euylypha, 29
Invertebrata, hosts of Gregarines, 97 f.
Iodine, 239
Iophon, 223
Iridogorgia, 355
Isaurus, 405
Ischadites, 207
Ischikawa, on syngamy of Cystoflagellates, 135;
on structure of Ephelota, 162
Isidae, 337, 353
Isidella, 354
Isis, 353
Ismailia, prophylaxis of malaria at, 106
Isochela (a chela divisible by each of two planes into two equal
parts, the two ends being equally developed), 222
Isocrinus—see Pentacrinus
Isogamy, 33 f.;
of Rhizopoda, 56 f.;
of Stephanosphaera, 128
—see also Syngamy
Isospores, 85;
of Radiolaria, 76;
of Collozoum inerme, 76

Jaekel, on Silurian Asteroidea and Ophiuroidea, 501;

on classification of Crinoidea, 589, 595;
on classification of Cystoidea, 598
James-Clark, on Protozoa, 46;
on Choanoflagellates, 121, 123;
on Sponges, 167
Jaw, of Ophiothrix fragilis, 482;
of fossil Ophiuroidea, 502;
of Echinus esculentus, 526
Jelly, forming theca in Flagellates, 113
Jelly-fish, 249, 297, 323
Jennings, on protoplasmic movements, 4 n., 16 n.;
reaction of Protista to repellent stimuli, 20 n., 21 n.
Jensen, on density of living protoplasm, 13 n.;
on protoplasmic movements, 16 n.
Joblot, on organisms of putrefaction, 43;
on Protozoa, 45
Joenia, 111
Johnson, 352 n.
Juncella, 330, 335, 357
Jung, 253 n.
Jungersen, 359 n.

Karyogamy, 34 n.
—see also Syngamy of Ciliata
Karyokinesis, 25, 26, 27;
 function of, 28 f.;
of micronuclei of Ciliata, 144 f.
—see also Mitosis
Karyolysus, 97
Karyosome, 24
Keeble, 175 n.
Keller, 233
Kemna, on stylopodium of Foraminifera, 60
Kent, Saville, on Choanoflagellates, 122 f., 182;
on Infusoria and Flagellates, 136 n.
Keroeides, 351
Kerona, 138;
K. polyporum, 158 n.
Kieselguhr, 87
Kirkpatrick, 215
Kishinouye, 313 n., 321 n., 333, 352
Klebs, on Flagellates, 119;
on Dinoflagellates, 130
Koch, von, on methods of cultivation of lower organisms, 44;
on malarial parasites, 103
Kölliker, on Sporozoa, 94 f.
Kophobelemnon, 362
Kophobelemnonidae, 362
Köppen, on Sticholonche and its parasite, Amoebophrya, 87 n.
Korethraster, 453, 463
Kowalevsky, 341, 422
Krukenberg, on pepsin in a Myxomycete, 16
Kükenthal, 363

Labbé, on Protozoa, 45;

monograph of Sporozoa, 102 n.
Labial plexus of Antedon rosacea, 587
Labyrinthine shell-wall of arenaceous Foraminifera, 59, 66
Labyrinthula, 90 f.
Lachmann, Claparède and, on Protozoa, 45;
on Suctoria, 162
Lacrymaria, 137, 152 n.;