Stochastic Global Optimization: Problem Classes and Solution Techniques
Journal of Global Optimization
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Improving the behavior of the genetic algorithm in a dynamic environment
Improving the behavior of the genetic algorithm in a dynamic environment
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking evolutionary algorithms: towards exploratory landscape analysis
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Multimodal optimization by means of a topological species conservation algorithm
IEEE Transactions on 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
Effects of scale-free and small-world topologies on binary coded self-adaptive CEA
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Improved topological niching for real-valued global optimization
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
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The performance of niching based or related evolutionary algorithms clearly depends on problem properties as e.g. the number of local optima of a problem. We assume there must be more such properties currently not taken into account and, following from practical experience, suggest two more, namely basin size contrast (BSC), the size relation of the largest and the smallest basin, and global to local optima contrast (GLC), the height relation of the global and an average local optimum. We investigate the effect of these problem properties on the performance of different basin identification methods (as subtasks of niching algorithms), namely nearest-better clustering, detect-multimodal, and Jarvis-Patrick clustering, individually, or in combinations. Employing an existing problem generator that enables complete control and knowledge of basins, instances are generated and validated according to predefined property values and the basin identification performance data is modeled in order to detect similarities that may be interpreted as effects of the stated properties. We also give recommendations concerning usage of basin identification methods in different situations. Our approach is strongly related to the recently suggested general idea of exploratory landscape analysis (ELA).