Memetic Algorithm for Intense Local Search Methods Using Local Search Chains
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Hybrid algorithms based on harmony search and differential evolution for global optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
SRDE: an improved differential evolution based on stochastic ranking
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Discrete differential evolution algorithm for the job shop scheduling problem
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Hybrid differential evolution based on fuzzy C-means clustering
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Computers and Operations Research
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
A probabilistic memetic framework
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
Enhancing differential evolution frameworks by scale factor local search: part II
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Varying number of difference vectors in differential evolution
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Multi-start JADE with knowledge transfer for numerical optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Adaptive population tuning scheme for differential evolution
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Optimized bi-dimensional data projection for clustering visualization
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An analysis of the migration rates for biogeography-based optimization
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We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.