Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
On Decentralizing Selection Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
IEEE Transactions on Evolutionary Computation
Centric selection: a way to tune the exploration/exploitation trade-off
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Polynomial selection scheme with dynamic parameter estimation in cellular genetic algorithm
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Dynamic Fault-Tolerant three-dimensional cellular genetic algorithms
Journal of Parallel and Distributed Computing
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In this paper we introduce a new selection scheme in cellular genetic algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows accurate control of the selective pressure. First we compare this new scheme with the classical rectangular grid shapes solution according to the selective pressure: we can obtain the same takeover time with the two techniques although the spreading of the best individual is different. We then give experimental results that show to what extent AS promotes the emergence of niches that support low coupling and high cohesion. Finally, using a cGA with anisotropic selection on a Quadratic Assignment Problem we show the existence of an anisotropic optimal value for which the best average performance is observed. Further work will focus on the selective pressure self-adjustment ability provided by this new selection scheme.