Journal of Global Optimization
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Two improved differential evolution schemes for faster global search
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
An enhanced memetic differential evolution in filter design for defect detection in paper production
Evolutionary Computation
Advances in Differential Evolution
Advances in Differential Evolution
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
A study on scale factor in distributed differential evolution
Information Sciences: an International Journal
Self-adaptive mutation in the differential evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Multi-agent simulated annealing algorithm based on differential evolution algorithm
International Journal of Bio-Inspired Computation
A study on scale factor/crossover interaction in distributed differential evolution
Artificial Intelligence Review
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This paper proposes a modification of Differential Evolution (DE) schemes. During the offspring generation, a local search is applied, with a certain probability to the scale factor in order to generate an offspring with high performance. In a memetic fashion, the main idea in this paper is that the application of a different perspective in the search of a DE can assist the evolutionary framework and prevent the undesired effect of stagnation which DE is subject to. Two local search algorithms have been tested for this purpose and an application to the individual with the best performance has been proposed. The resulting algorithms seem to significantly enhance the performance of a standard DE scheme over a broad set of test problems. Numerical results show that the modified algorithm is very efficient with respect to a standard DE in terms of final solution detected, convergence speed and robustness.