Hybrid Evolutionary Algorithm for Solving Global Optimization Problems
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Geometric nelder-mead algorithm on the space of genetic programs
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A metamodel-assisted evolutionary algorithm for expensive optimization
Journal of Computational and Applied Mathematics
Geometric generalization of the nelder-mead algorithm
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
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In this paper, a new real-coded evolutionary algorithm-- low dimensional simplex evolution (LDSE) for global optimization is proposed. It is a hybridization of two well known heuristics, the differential evolution (DE) and the Nelder-Mead method. LDSE takes the idea of DE to randomly select parents from the population and perform some operations with them to generate new individuals. Instead of using the evolutionary operators of DE such as mutation and cross-over, we introduce operators based on the simplex method, which makes the algorithm more systematic and parameter-free. The proposed algorithm is very easy to implement, and its efficiency has been studied on an extensive testbed of 50 test problems from [1]. Numerical results show that the new algorithm outperforms DE in terms of number of function evaluations (nfe) and percentage of success (ps).