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Michal Przewozniczek
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2020 – today
- 2024
- [j18]Renato Tinós
, Michal Witold Przewozniczek
Iterated Local Search with Linkage Learning. ACM Trans. Evol. Learn. Optim. 4(2): 7 (2024) - [c34]Marcin Michal Komarnicki
Overlapping Cooperative Co-Evolution for Overlapping Large-Scale Global Optimization Problems. GECCO 2024 - [c33]Lukasz Tulczyjew
CANNIBAL Unveils the Hidden Gems: Hyperspectral Band Selection via Clustering of Weighted Variable Interaction Graphs. GECCO 2024 - [i4]Michal Witold Przewozniczek, Bartosz Frej, Marcin M. Komarnicki:
The hop-like problem nature - unveiling and modelling new features of real-world problems. CoRR abs/2406.01215 (2024) - [i3]Renato Tinós, Michal Przewozniczek, Darrell Whitley, Francisco Chicano:
Iterated Local Search with Linkage Learning. CoRR abs/2410.01583 (2024) - 2023
- [j17]Marcin Michal Komarnicki
Incremental Recursive Ranking Grouping for Large-Scale Global Optimization. IEEE Trans. Evol. Comput. 27(5): 1498-1513 (2023) - [c32]Marcin Michal Komarnicki
Incremental Recursive Ranking Grouping - A Decomposition Strategy for Additively and Nonadditively Separable Problems. GECCO Companion 2023: 27-28 - [c31]Michal Witold Przewozniczek
First Improvement Hill Climber with Linkage Learning - on Introducing Dark Gray-Box Optimization into Statistical Linkage Learning Genetic Algorithms. GECCO 2023: 946-954 - [c30]Michal Witold Przewozniczek
To slide or not to slide? Moving along fitness levels and preserving the gene subsets diversity in modern evolutionary computation. GECCO 2023: 955-962 - [c29]Renato Tinós
Genetic Algorithm with Linkage Learning. GECCO 2023: 981-989 - 2022
- [c28]Michal Witold Przewozniczek, Marcin M. Komarnicki:
Empirical linkage learning for non-binary discrete search spaces in the optimization of a large-scale real-world problem. GECCO Companion 2022: 35-36 - [c27]Michal Witold Przewozniczek, Renato Tinós, Bartosz Frej, Marcin M. Komarnicki:
On turning black - into dark gray-optimization with the direct empirical linkage discovery and partition crossover. GECCO 2022: 269-277 - [c26]Renato Tinós, Michal Witold Przewozniczek, Darrell Whitley:
Iterated local search with perturbation based on variables interaction for pseudo-boolean optimization. GECCO 2022: 296-304 - [i2]Marcin Michal Komarnicki, Michal Witold Przewozniczek, Halina Kwasnicka
Incremental Recursive Ranking Grouping for Large Scale Global Optimization. CoRR abs/2206.04168 (2022) - 2021
- [j16]Michal Witold Przewozniczek
Empirical problem decomposition - the key to the evolutionary effectiveness in solving a large-scale non-binary discrete real-world problem. Appl. Soft Comput. 113(Part): 107864 (2021) - [j15]Michal Witold Przewozniczek
Multi-Objective parameter-less population pyramid for solving industrial process planning problems. Swarm Evol. Comput. 60: 100773 (2021) - [c25]Michal Witold Przewozniczek
Fitness Caching - From a Minor Mechanism to Major Consequences in Modern Evolutionary Computation. CEC 2021: 1785-1791 - [c24]Michal Witold Przewozniczek
Multi-objective parameter-less population pyramid in solving the real-world and theoretical problems. GECCO Companion 2021: 41-42 - [c23]Michal Witold Przewozniczek
Direct linkage discovery with empirical linkage learning. GECCO 2021: 609-617 - [c22]Michal Witold Przewozniczek
Hybrid linkage learning for permutation optimization with Gene-pool optimal mixing evolutionary algorithms. GECCO Companion 2021: 1442-1450 - 2020
- [j14]Michal Witold Przewozniczek
Metaheuristic algorithms with solution encoding mixing for effective optimization of SDM optical networks. Eng. Appl. Artif. Intell. 95: 103843 (2020) - [j13]Michal Witold Przewozniczek
Splitting the fitness and penalty factor for temporal diversity increase in practical problem solving. Expert Syst. Appl. 145: 113126 (2020) - [j12]Michal Witold Przewozniczek
Subpopulation initialization driven by linkage learning for dealing with the Long-Way-To-Stuck effect. Inf. Sci. 521: 62-80 (2020) - [j11]Piotr Dziurzanski
Scalable distributed evolutionary algorithm orchestration using Docker containers. J. Comput. Sci. 40: 101069 (2020) - [j10]Michal Witold Przewozniczek
Empirical Linkage Learning. IEEE Trans. Evol. Comput. 24(6): 1097-1111 (2020) - [c21]Michal Witold Przewozniczek
Empirical linkage learning. GECCO Companion 2020: 23-24 - [c20]Marcin M. Komarnicki
Comparative mixing for DSMGA-II. GECCO 2020: 708-716 - [c19]Michal Witold Przewozniczek
On measuring and improving the quality of linkage learning in modern evolutionary algorithms applied to solve partially additively separable problems. GECCO 2020: 742-750 - [c18]Szymon Wozniak
Parameter-Less Population Pyramid for Permutation-Based Problems. PPSN (1) 2020: 418-430 - [i1]Michal Witold Przewozniczek, Piotr Dziurzanski, Shuai Zhao, Leandro Soares Indrusiak:
Multi-Objective Parameter-less Population Pyramid for Solving Industrial Process Planning Problems. CoRR abs/2009.08929 (2020)
2010 – 2019
- 2019
- [j9]Michal Witold Przewozniczek
Universal strategy of dynamic subpopulation number management in practical network optimization problems. Appl. Soft Comput. 82 (2019) - [j8]Michal Witold Przewozniczek
The transformation of the k-Shortest Steiner trees search problem into binary dynamic problem for effective evolutionary methods application. Inf. Sci. 479: 1-19 (2019) - [c17]Marcin M. Komarnicki
Parameter-less, population-sizing DSMGA-II. GECCO (Companion) 2019: 289-290 - [c16]Adam M. Zielinski, Marcin M. Komarnicki
Parameter-less population pyramid with automatic feedback. GECCO (Companion) 2019: 312-313 - [c15]Shuai Zhao, Piotr Dziurzanski
Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories. GECCO 2019: 1381-1389 - [c14]Shuai Zhao, Haitao Mei, Piotr Dziurzanski
Cloud-Based Integrated Process Planning and Scheduling Optimisation via Asynchronous Islands. GECON 2019: 247-259 - 2018
- [c13]Michal Przewozniczek
The Practical Use of Problem Encoding Allowing Cheap Fitness Computation of Mutated Individuals. FedCSIS 2018: 57-65 - [c12]Michal Witold Przewozniczek
The influence of fitness caching on modern evolutionary methods and fair computation load measurement. GECCO (Companion) 2018: 241-242 - 2017
- [j7]Michal Witold Przewozniczek
The evolutionary cost of Baldwin effect in the routing and spectrum allocation problem in elastic optical networks. Appl. Soft Comput. 52: 843-862 (2017) - [c11]Michal Witold Przewozniczek
The Effectiveness of the Simplicity in Evolutionary Computation. ACIIDS (2) 2017: 392-402 - [c10]Michal Witold Przewozniczek
Problem Encoding Allowing Cheap Fitness Computation of Mutated Individuals. CEC 2017: 308-316 - [c9]Marcin M. Komarnicki, Michal Witold Przewozniczek:
Parameter-less population pyramid with feedback. GECCO (Companion) 2017: 109-110 - 2016
- [j6]Michal Przewozniczek
Active Multi-Population Pattern Searching Algorithm for flow optimization in computer networks - The novel coevolution schema combined with linkage learning. Inf. Sci. 355-356: 15-36 (2016) - [c8]Michal Przewozniczek
Dynamic Subpopulation Number Control for Solving Routing and Spectrum Allocation Problems in Elastic Optical Networks. ENIC 2016: 257-264 - [c7]Marcin Komarnicki
Linked Genes Migration in Island Models. IJCCI (ECTA) 2016: 30-40 - 2015
- [j5]Michal Przewozniczek
Towards solving practical problems of large solution space using a novel pattern searching hybrid evolutionary algorithm - An elastic optical network optimization case study. Expert Syst. Appl. 42(21): 7781-7796 (2015) - [c6]Michal Przewozniczek
Towards Finding an Effective Uniform and Single Point Crossover Balance for Optimization of Elastic Optical Networks. ENIC 2015: 40-46 - [c5]Pawel B. Myszkowski
Constructive heuristics for technology-driven Resource Constrained Scheduling Problem. FedCSIS 2015: 119-127 - [c4]Michal Przewozniczek
Multi Population Pattern Searching Algorithm for Solving Routing Spectrum Allocation with Joint Unicast and Anycast Problem in Elastic Optical Networks. IDEAL 2015: 328-339 - [c3]Bartosz Andrzej Fidrysiak, Michal Przewozniczek
Towards Finding an Effective Way of Discrete Problems Solving: The Particle Swarm Optimization, Genetic Algorithm and Linkage Learning Techniques Hybrydization. IJCCI (ECTA) 2015: 228-236 - 2013
- [j4]Krzysztof Walkowiak
Heuristic Algorithms for Survivable P2P Multicasting. Appl. Artif. Intell. 27(4): 278-303 (2013) - 2011
- [j3]Krzysztof Walkowiak
Modeling and optimization of survivable P2P multicasting. Comput. Commun. 34(12): 1410-1424 (2011) - [j2]Michal Przewozniczek, Krzysztof Walkowiak
Optimizing distributed computing systems for k-nearest neighbours classifiers - evolutionary approach. Log. J. IGPL 19(2): 357-372 (2011) - [j1]Halina Kwasnicka
Multi Population Pattern Searching Algorithm: A New Evolutionary Method Based on the Idea of Messy Genetic Algorithm. IEEE Trans. Evol. Comput. 15(5): 715-734 (2011)
2000 – 2009
- 2007
- [c2]Michal Przewozniczek
Quasi-hierarchical Evolutionary Algorithm for Flow Optimization in Survivable MPLS Networks. ICCSA (3) 2007: 330-342 - 2005
- [c1]Michal Przewozniczek
Evolutionary Algorithm for Congestion Problem in Connection-Oriented Networks. ICCSA (4) 2005: 802-811
Coauthor Index
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