How to detect all maxima of a function
Theoretical aspects of evolutionary computing
Journal of Global Optimization
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Evolutionary Computation for Modeling and Optimization
Evolutionary Computation for Modeling and Optimization
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Statistical distribution of the convergence time of evolutionaryalgorithms for long-path problems
IEEE Transactions on Evolutionary Computation
Are evolutionary algorithm competitions characterizing landscapes appropriately
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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
Evolutionary algorithm characterization in real parameter optimization problems
Applied Soft Computing
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This paper deals with the characterization of the fitness landscape of multimodal functions and how it can be used to choose the most appropriate evolutionary algorithm for a given problem. An algorithm that obtains a general description of real valued multimodal fitness landscapes in terms of the relative number of optima, their sparseness, the size of their attraction basins and the evolution of this size when moving away from the global optimum is presented and used to characterize a set of well-known multimodal benchmark functions. To illustrate the relevance of the information obtained and its relationship to the performance of evolutionary algorithms over different fitness landscapes, two evolutionary algorithms, Differential Evolution and Covariance Matrix Adaptation, are compared over the same benchmark set showing their behavior depending on the multimodal features of each landscape.