Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An investigation of niche and species formation in genetic function optimization
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Improving Genetic Algorithms with Sharing through Cluster Analysis
Proceedings of the 5th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Niche radius adaptation in the CMA-ES niching algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
| Hi-index | 0.00 |
In the field of Genetic Algorithms, nichingtechniques have been invented with the aim to induce speciationon multimodal fitness landscapes. Unfortunately, they often rely on a problem-dependent niche radiusparameter. This is the niche radius problem. In recent research, the possibilities to transfer niching techniques to the field of Evolution Strategies(ES) have been studied. First attempts were carried out to learn a good value for the niche radius through self-adaptation. In this paper we introduce a new niching method for ES with self-adaptation of the niche radius: asymmetric sharing. It is a form of fitness sharing. In contrast to earlier studies, it does not depend on coupling the niche radius to other strategy parameters. Experimental results indicate that asymmetric sharing performs well in comparison to traditional sharing, without relying on problem-dependent parameters.