Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Exploring dynamic self-adaptive populations in differential evolution
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Performance comparison of self-adaptive and adaptive differential evolution algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Advances in Differential Evolution
Advances in Differential Evolution
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Enhancing differential evolution frameworks by scale factor local search: part I
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
Scale factor inheritance mechanism in distributed differential evolution
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Solving rotated multi-objective optimization problems using differential evolution
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
IEEE Transactions on Evolutionary Computation
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The Differential Evolution (DE) algorithm is an efficient and powerful evolutionary algorithm (EA) for solving optimization problems. However the success of DE in solving a specific problem is closely related to appropriately choosing its control parameters. Parameter tuning leads to additional computational costs because of time-consuming trial-and-error tests. Self-adaptation, in contrast, allows the algorithm to reconfigure itself, automatically adapting to the problem being solved. There are in the literature some self-adaptive versions of differential evolution, however they do not align completely with self-adaptation concepts. In this paper, some self-adaptive versions of DE in the literature are described and discussed, and then a new Self-Adaptive Differential Evolution with multiple mutation strategies is proposed; it is called Self-adaptive Mutation Differential Evolution (SaMDE) and aims at preserving the essential characteristics of self-adaptation. Some computational experiments which illustrate algorithm behaviour and a comparative test with the classical DE and with an important self-adaptive DE are presented. The results suggest that SaMDE is a very promising algorithm.