Population variation in genetic programming
Information Sciences: an International Journal
Particle swarm optimization based on dynamic niche technology with applications to conceptual design
Advances in Engineering Software
Heuristic speciation for evolving neural network ensemble
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Engineering Applications of Artificial Intelligence
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Adaptive niche radii and niche shapes approaches for niching with the cma-es
Evolutionary Computation
Dynamics of fitness sharing evolutionary algorithms for coevolution of multiple species
Applied Soft Computing
A dual-population genetic algorithm for adaptive diversity control
IEEE Transactions on Evolutionary Computation
Speciation in evolutionary algorithms: adaptive species discovery
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Niche radius adaptation in the CMA-ES niching algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A novel type of niching methods based on steady-state genetic algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Evolution of cooperating ANNs through functional phenotypic affinity
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Applied Soft Computing
A Review of Niching Genetic Algorithms for Multimodal Function Optimization
Cybernetics and Systems Analysis
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We propose a characterization of the dynamic behavior of an evolutionary algorithm (EA) with fitness sharing as a function of both the niche radius and the population size. Such a characterization, given in terms of the mean and the standard deviation of the number of niches found during the evolution, can be applied to any EA employing a proportional selection mechanism and does not make any assumption on either the fitness landscape or the internal parameters of the EA itself. On the basis of the proposed characterization, a method for estimating the optimal values for the population size and the niche radius without any a priori information on the fitness landscape is presented and tested on a standard set of functions. The proposed method also provides the best solution for the problem at hand, i.e., the solution obtained in correspondence of such optimal values, at no additional cost.