Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Noisy Environments
Machine Learning
Evolution Strategies on Noisy Functions: How to Improve Convergence Properties
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A Robust Solution Searching Scheme in Genetic Search
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
Robust design of multilayer optical coatings by means ofevolutionary algorithms
IEEE Transactions on Evolutionary Computation
Exploiting overlap when searching for robust optima
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
An archive maintenance scheme for finding robust solutions
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Using the uncertainty handling CMA-ES for finding robust optima
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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We have proposed a scheme that extends the application of GAs to domains that require detection of robust solutions. We called this technique GAs/RS3 - GAs with a robust solution searching scheme. In the GAs/RS3, a perturbation is added to the phenotypic feature once for evaluation of an individual, thereby reducing the chance of selecting sharp peaks. We refer to this method as a single-evaluation model (SEM). In this chapter, we introduce a natural variant of this method, a multi-evaluation-model (MEM), where perturbations are given more than once for evaluation of the individual, and we offer comparative studies on their convergence property. The results showed that for the GAs/RS with SEM the population converges to robust solutions faster than with the MEM, and as the number of evaluations increases, the convergence speed decreases. We may conclude that the GAs/RS3 with the SEM is more efficient than with the MEM. We also introduced a variation of the MEM, i.e., multievaluation model keeping the worst value (MEM-W), and provided a mathematical analysis.