Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
Differential mutation based on population covariance matrix
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
IEEE Transactions on Evolutionary Computation
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Metaoptimization is a way of tuning parameters of an optimization algorithm with use of a higher-level optimizer. In this paper it is applied to the problem of choosing among possible mutation range adaptation schemes in Differential Evolution (DE). We consider a new version of DE, called DE/rand/∞. In this algorithm, differential mutation is replaced by a Gaussian one, where the covariance matrix is determined from the contents of the current population. We exploit this property to separate the adaption of search directions from the adaptation of mutation range. The former is characterized by a norm of the covariance matrix while the latter can be expressed as a normed covariance matrix multiplied by the scaling factor. Such separation allows us to introduce a few schemes of direct, explicit control of the mutation range and to compare them with the basic, implicit scheme present in DE/rand/∞. To ensure fair comparisons all versions of DE/rand/∞ are first metaoptimized and then assessed on the CEC'05 benchmark.