Cooperative Co-evolution with Online Optimizer Selection for Large-Scale Optimization

Sun, Y, Kirley, M and Li, X 2018, 'Cooperative Co-evolution with Online Optimizer Selection for Large-Scale Optimization', in Proceedings of the 2018 Conference on Genetic and Evolutionary Computation Conference (GECCO), Kyoto, Japan, 15-19 July 2018, pp. 1079-1086.


Document type: Conference Paper
Collection: Conference Papers

Title Cooperative Co-evolution with Online Optimizer Selection for Large-Scale Optimization
Author(s) Sun, Y
Kirley, M
Li, X
Year 2018
Conference name Proceedings of the 2018 Conference on Genetic and Evolutionary Computation Conference (GECCO)
Conference location Kyoto, Japan
Conference dates 15-19 July 2018
Proceedings title Proceedings of the 2018 Conference on Genetic and Evolutionary Computation Conference (GECCO)
Publisher ACM
Place of publication New York
Start page 1079
End page 1086
Total pages 8
Abstract Cooperative co-evolution (CC) is an effective framework that can be used to solve large-scale optimization problems. It typically divides a problem into components and uses one optimizer to solve the components in a round-robin fashion. However the relative contribution of each component to the overall fitness value may vary. Furthermore, using one optimizer may not be sufficient when solving a wide range of components with different characteristics. In this paper, we propose a novel CC framework which can select an appropriate optimizer to solve a component based on its contribution to the fitness improvement. In each evolutionary cycle, the candidate optimizer and component that make the greatest contribution to the fitness improvement are selected for evolving. We evaluated the efficacy of the proposed CC with Optimizer Selection (CCOS) algorithm using large-scale benchmark problems. The numerical experiments showed that CCOS outperformed the CC model without optimizer selection ability. When compared against several other state-of-the-art algorithms, CCOS generated competitive solution quality.
Subjects Optimisation
Neural, Evolutionary and Fuzzy Computation
Keyword(s) Large scale optimization
divide and conquer
theory of computation
Copyright notice © 2018 Association for Computing Machinery.
ISBN 9781450356183
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