Journal of Global Optimization
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
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Memetic algorithm with local search chaining for large scale continuous optimization problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
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In this paper, we propose an approach for measuring the level of separability (in a relaxed sense) among variables, making use of the rectangle condition for separable functions. This approach is then used in a differential evolution-based algorithm for high dimensional optimization. The decision variables are associated into groups by their estimated level of separability. Such estimation is refined throughout generations, depending on the area being currently explored. Results are shown from 50 to 10,000 variables. The experiments are performed with unimodal, multimodal, separable and non-separable functions. Comparison are shown with differential evolution alone, and with other algorithms of the state of the art.