A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A Parallel Differential Evolution Algorithm A Parallel Differential Evolution Algorithm
PARELEC '06 Proceedings of the international symposium on Parallel Computing in Electrical Engineering
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
An enhanced memetic differential evolution in filter design for defect detection in paper production
Evolutionary Computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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
Distributed differential evolution with explorative---exploitative population families
Genetic Programming and Evolvable Machines
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Scale factor inheritance mechanism in distributed differential evolution
Soft Computing - A Fusion of Foundations, Methodologies and Applications
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
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This paper proposes the use of two algorithms based on the parallel differential evolution. The first algorithm proposes the use of endemic control parameters within a parallel differential evolution algorithm; the differential evolution running at each subpopulation is associated with randomly initialised scale factor and crossover rate, which are then repeatedly updated during the optimisation process. The second algorithm proposes decomposing the search space of large-scale problems into lower-dimensionality subspaces, and associating each of these to one subpopulation of a parallel differential evolution algorithm. Each subpopulation is running a modified differential evolution algorithm, where the crossover function is limited to components of the subpopulation's associated subspace. According to numerical results, both algorithms seem to be clear improvements over the original parallel distributed evolution; they are simple, robust, and efficient algorithms suited for various applications.