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Hitoshi Iima
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2020 – today
- 2024
- [j2]Yasuaki Kuroe
, Hitoshi Iima
Classes of Dilemma Problems and Their Multi-Agent Reinforcement Learning Method. IEEE Access 12: 107353-107367 (2024) - [c36]Anamika Jha, Hitoshi Iima:
CT to MRI Image Translation Using CycleGAN: A Deep Learning Approach for Cross-Modality Medical Imaging. ICAART (3) 2024: 951-957 - 2023
- [c35]Shintaro Tanaka, Hitoshi Iima:
Accelerated Text Data Augmentation Using a Paraphrase Generation Model with Round-Trip Translation as a Supervisor. ICMLA 2023: 1090-1093 - 2022
- [c34]Hitoshi Iima, Yoshiyuki Nakamura:
Hyperheuristic Method Based on Deep Reinforcement Learning. IIAI-AAI 2022: 303-306 - [c33]Hitoshi Iima, Yohei Hazama:
Genetic Algorithm with Machine Learning to Estimate the Optimal Objective Function Values of Subproblems. ISMSI 2022: 1-8 - [c32]Riki Kataoka, Yoshiyuki Karuno, Hitoshi Iima:
Drone Scheduling for Parcel Delivery with a Given Truck Route and Setup Times. SCIS/ISIS 2022: 1-6 - 2020
- [c31]Takanobu Yaguchi, Hitoshi Iima:
Design of an Artificial Game Entertainer by Reinforcement Learning. CoG 2020: 588-591 - [c30]Yasuaki Kuroe, Hitoshi Iima, Yutaka Maeda:
Four Models of Hopfield-Type Octonion Neural Networks and Their Existing Conditions of Energy Functions. IJCNN 2020: 1-7
2010 – 2019
- 2019
- [c29]Hitoshi Iima, Hiroya Oonishi:
Deep Learning for Designing an AI Player of the Puzzle Game Geometry Friends. CSII 2019: 31-36 - [c28]Kazuki Nishisaka, Hitoshi Iima:
Real-Coded Genetic Algorithm Realizing Fast Convergence by Reducing Its Population Size. ICTAI 2019: 1767-1770 - [c27]Hitoshi Iima, Keijiro Yokoyama:
Differential Evolution Using Linkage Identification. ICTAI 2019: 1791-1794 - [c26]Yasuaki Kuroe, Hitoshi Iima, Yutaka Maeda:
Learning Method of Recurrent Spiking Neural Networks to Realize Various Firing Patterns using Particle Swarm Optimization. IJCCI 2019: 479-486 - 2018
- [c25]Takahiro Watanabe, Hitoshi Iima:
Nonlinear Optimization Method Based on Stochastic Gradient Descent for Fast Convergence. SMC 2018: 4198-4203 - 2017
- [c24]Teruhisa Hochin, Hiroki Nomiya
A Smart Framework towards Providing Benefits for One and All. ACIT/CSII/BCD 2017: 129-132 - [c23]Hiroya Oonishi, Hitoshi Iima:
Improving generalization ability in a puzzle game using reinforcement learning. CIG 2017: 232-239 - 2016
- [c22]Yasuaki Kuroe, Hitoshi Iima:
A model of Hopfield-type octonion neural networks and existing conditions of energy functions. IJCNN 2016: 4426-4430 - 2015
- [c21]Hitoshi Iima, Yasuaki Kuroe:
Swarm reinforcement learning methods improving certainty of learning for a multi-robot formation problem. CEC 2015: 3026-3033 - [c20]Hitoshi Iima, Kazuhiro Shin-ike:
Genetic Algorithms for Student Grouping Problems. SMC 2015: 2902-2907 - 2014
- [c19]Hitoshi Iima, Yasuaki Kuroe:
Multi-objective reinforcement learning for acquiring all Pareto optimal policies simultaneously - Method of determining scalarization weights. SMC 2014: 876-881 - 2013
- [c18]Hitoshi Iima, Yasuaki Kuroe:
Swarm Reinforcement Learning Method for a Multi-robot Formation Problem. SMC 2013: 2298-2303 - 2012
- [c17]Yusuke Mukai, Yasuaki Kuroe, Hitoshi Iima:
Multi-objective reinforcement learning method for acquiring all pareto optimal policies simultaneously. SMC 2012: 1917-1923 - 2011
- [c16]Kazuhiro Shin-ike, Hitoshi Iima:
A Method for Determining Classroom Seating Arrangements by Using Bioinformatics. Edutainment 2011: 106 - [c15]Yasuaki Kuroe, Shinpei Tanigawa, Hitoshi Iima:
Models of Hopfield-Type Clifford Neural Networks and Their Energy Functions - Hyperbolic and Dual Valued Networks -. ICONIP (1) 2011: 560-569 - [c14]Hitoshi Iima, Yasuaki Kuroe, Kazuo Emoto:
Swarm reinforcement learning methods for problems with continuous state-action space. SMC 2011: 2173-2180 - 2010
- [c13]Hitoshi Iima, Yasuaki Kuroe:
Swarm Reinforcement Learning Method Based on an Actor-Critic Method. SEAL 2010: 279-288 - [c12]Hitoshi Iima, Yasuaki Kuroe, Shoko Matsuda:
Swarm reinforcement learning method based on ant colony optimization. SMC 2010: 1726-1733
2000 – 2009
- 2009
- [c11]Hitoshi Iima, Yasuaki Kuroe:
Swarm Reinforcement Learning Algorithm Based on Particle Swarm Optimization Whose Personal Bests Have Lifespans. ICONIP (2) 2009: 169-178 - [c10]Kazuhiro Shin-ike, Hitoshi Iima:
A method for Development of collaborative learning by using a neural network and a genetic algorithm. ISADS 2009: 417-422 - 2008
- [c9]Hitoshi Iima, Yasuaki Kuroe:
Swarm reinforcement learning algorithms based on particle swarm optimization. SMC 2008: 1110-1115 - 2006
- [c8]Hitoshi Iima:
Parallel genetic algorithm for a multiobjective rescheduling problem. Artificial Intelligence and Soft Computing 2006: 273-278 - [c7]Yasuaki Kuroe, Hitoshi Iima:
A Learning Method for Synthesizing Spiking Neural Oscillators. IJCNN 2006: 3882-3886 - 2005
- [c6]Hitoshi Iima:
Proposition of Selection Operation in a Genetic Algorithm for a Job Shop Rescheduling Problem. EMO 2005: 721-735 - 2003
- [c5]Hitoshi Iima, T. Yakawa:
A new design of genetic algorithm for bin packing. IEEE Congress on Evolutionary Computation 2003: 1044-1049 - 2001
- [c4]Hitoshi Iima, Nobuo Sannomiya:
Module type genetic algorithm for modified scheduling problems with worker allocation. ACC 2001: 856-861
1990 – 1999
- 1999
- [j1]Hitoshi Iima, Ryoichi Kudo, Nobuo Sannomiya, Yasunori Kobayashi:
An autonomous decentralized scheduling algorithm for a scheduling problem in a metal mould assembly process. J. Intell. Manuf. 10(2): 161-167 (1999) - [c3]Hitoshi Iima, Tetsuya Hara, Norihisa Ichimi, Nobuo Sannomiya:
Autonomous Decentralized Scheduling Algorithm for a Job-Shop Scheduling Problem with Complicated Constraints. ISADS 1999: 366-369 - 1997
- [c2]Hitoshi Iima, Ryoichi Kudo, Nobuo Sannomiya, Yasunori Kobayashi:
An autonomous decentralized scheduling algorithm for a job shop process with a multi-function machine in parallel. ISADS 1997: 69-74 - 1996
- [c1]Nobuo Sannomiya, Hitoshi Iima:
Application of Genetic Algorithm to Scheduling Problems in Manufacturing Processes. International Conference on Evolutionary Computation 1996: 523-528
Coauthor Index
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