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.
Original language | English |
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Title of host publication | Proceedings of the 2018 Genetic and Evolutionary Computation Conference |
Subtitle of host publication | 2018 Genetic and Evolutionary Computation Conference, GECCO 2018; Kyoto; Japan; 15 July 2018 through 19 July 2018 |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1079-1086 |
Number of pages | 8 |
ISBN (Electronic) | 9781450356183 |
DOIs | |
Publication status | Published - 2 Jul 2018 |
Externally published | Yes |
Event | The Genetic and Evolutionary Computation Conference 2018 - Kyoto, Japan Duration: 15 Jul 2018 → 19 Jul 2018 Conference number: 20th http://gecco-2018.sigevo.org/index.html/tiki-index.php https://dl.acm.org/doi/proceedings/10.1145/3205455 (Proceedings) |
Conference
Conference | The Genetic and Evolutionary Computation Conference 2018 |
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Abbreviated title | GECCO 2018 |
Country/Territory | Japan |
City | Kyoto |
Period | 15/07/18 → 19/07/18 |
Other | The Genetic and Evolutionary Computation Conference (GECCO) presents the latest high-quality results in genetic and evolutionary computation since 1999. Topics include: genetic algorithms, genetic programming, ant colony optimization and swarm intelligence, complex systems (artificial life/robotics/evolvable hardware/generative and developmental systems/artificial immune systems), digital entertainment technologies and arts, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, evolutionary numerical optimization, real world applications, search-based software engineering, theory and more. |
Internet address |
Keywords
- Algorithm hybridization
- Algorithm selection
- Cooperarive co-evolution
- Large-scale optimization
- Resources allocation