Authors:
Bartosz Andrzej Fidrysiak
and
Michal Przewozniczek
Affiliation:
Wroclaw University of Technology, Poland
Keyword(s):
PSO for Binary Problems, Genetic Algorithms, Coevolution, Deceptive Functions, Linkage Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Co-Evolution and Collective Behavior
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Hybrid Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
;
Swarm/Collective Intelligence
Abstract:
Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are well known optimization tools. PSO
advantage is its capability for fast convergence to the promising solutions. On the other hand GAs are able
to process schemata thanks to the use of crossover operator. However, both methods have also their
drawbacks – PSO may fall into the trap of preconvergence, while GA capability of fast finding locally
optimal (or close to optimal) solutions seems low when compared to PSO. Relatively new, important
research direction in the field of Evolutionary Algorithms is linkage learning. The linkage learning methods
gather the information about possible gene dependencies and use it to improve their effectiveness. Recently,
the linkage learning evolutionary methods were shown to be effective tools to solve both: theoretical and
practical problems. Therefore, this paper proposes a PSO and GA hybrid, improved by the linkage learning
mechanisms, dedicated to solve binary problems. The proposed
method tries to combine the GA schema
processing ability, linkage information processing and uses fast PSO convergence to quickly improve the
quality of already known solutions.
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