Computers and Operations Research
Journal of Global Optimization
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
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Advances in Differential Evolution
Advances in Differential Evolution
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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This paper presents a novel differential evolution algorithm based on variable parameter search to solve real-parameter continuous function optimization problems. In order to provide differential evolution algorithm with local intensification capability, each trial individual is generated by a variable parameter search procedure using variable mutation scale factor and crossover rate as well as (possibly) variable mutation strategies. The novelty stems from the fact that while a pure differential evolution algorithm achieves global exploration during the search process, variable parameter search procedure intensifies the search around local minima by using traditional DE mutation and crossover operators as well as variable mutation strategies. The algorithm was tested using benchmark instances designed for a special session in CEC05 and other instaces from the literature. The experimental results show its highly competitive performance against the very recent differential evolution algorithm with local search by Noman and Iba in [1] (IEEE Transaction on Evolutionary Computation, Vol. 12, No. 1, pp. 107-125, February 2008).