`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Memetic algorithms: a short introduction
New ideas in optimization
Handbook of Memetic Algorithms
Handbook of Memetic Algorithms
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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MEMPSODE global optimization software tool integrates Particle Swarm Optimization, a prominent population-based stochastic algorithm, with well established efficient local search procedures. In the original description of the algorithm [17] a single local search with specific parameters was applied at selected best position vectors. In this work we present an adaptive variant of MEMPSODE where the local search is selected from a predefined pool of different algorithms. The choice of each local search is based on a probabilistic strategy that uses a simple metric to score the efficiency of the local search. This new version of the algorithm, Adapt-MEMPSODE, is benchmarked against BBOB 2013 test bed. The results show great improvement with respect to the static version that was also benchmarked in earlier workshop.