GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Adaptive niche radii and niche shapes approaches for niching with the cma-es
Evolutionary Computation
Niching the CMA-ES via nearest-better clustering
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Multimodal optimization by means of a topological species conservation algorithm
IEEE Transactions on Evolutionary Computation
Niching foundations: basin identification on fixed-property generated landscapes
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Diversified virtual camera composition
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
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We show how nearest-better clustering, the core component of the NBC-CMA niching evolutionary algorithm, is improved by appyling a second heuristic rule. This leads to enhanced basin identification for higher dimensional (5D to 20D) optimization problems, where the NBC-CMA has previously shown only mediocre performance compared to other niching and global optimization algorithms. The new method is integrated into a niching algorithm (NEA2) and compared to NBC-CMA and BIPOP-CMA-ES via the BBOB benchmarking suite. It performs very well on problems that enable recognizing basins at all with reasonable effort (number of basins not too high, e.g. the Gallagher problems), as expected. Beyond that point, niching is obviously not applicable any more and random restarts as done by the CMA-ES are the method of choice.