Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Step-Size Adaption Based on Non-Local Use of Selection Information
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
On the multilevel structure of global optimization problems
Computational Optimization and Applications
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Global Optimization of Morse Clusters by Potential Energy Transformations
INFORMS Journal on Computing
Large Barrier Trees for Studying Search
IEEE Transactions on Evolutionary Computation
pCMALib: a parallel fortran 90 library for the evolution strategy with covariance matrix adaptation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on 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
Memetic algorithms for continuous optimisation based on local search chains
Evolutionary Computation
Global characterization of the CEC 2005 fitness landscapes using fitness-distance analysis
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Comparing the niches of CMA-ES, CHC and pattern search using diverse benchmarks
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
SIAM Journal on Scientific Computing
A meta-learning prediction model of algorithm performance for continuous optimization problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Length scale for characterising continuous optimization problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Evolutionary algorithm characterization in real parameter optimization problems
Applied Soft Computing
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
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An algorithm independent metric is introduced that measures the dispersion of a uniform random sample drawn from the top ranked percentiles of the search space. A low dispersion function is one where the dispersion decreases as the sample is restricted to better regions of the search space. A high dispersion function is one where dispersion stay constant or increases as the sample is restricted to better regions of the search space. This distinction can be used to explain why the CMA Evolution Strategy is more efficient on some multimodal problems than on others.