CBCC3 — A contribution-based cooperative co-evolutionary algorithm with improved exploration/exploitation balance

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Authors

Colleges, School and Institutes

External organisations

  • RMIT University

Abstract

Cooperative Co-evolution (CC) is a promising framework for solving large-scale optimization problems. However, the round-robin strategy of CC is not an efficient way of allocating the available computational resources to components of imbalanced functions. The imbalance problem happens when the components of a partially separable function have non-uniform contributions to the overall objective value. Contribution-Based Cooperative Co-evolution (CBCC) is a variant of CC that allocates the available computational resources to the individual components based on their contributions. CBCC variants
(CBCC1 and CBCC2) have shown better performance than the standard CC in a variety of cases. In this paper, we show that over-exploration and over-exploitation are two major sources of performance loss in the existing CBCC variants. On that basis, we propose a new contribution-based algorithm that maintains a better balance between exploration and exploitation. The
empirical results show that the new algorithm is superior to its predecessors as well as the standard CC.

Details

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE Congress on Evolutionary Computation
Publication statusPublished - 24 Jul 2016
Event2016 IEEE Congress on Evolutionary Computation (CEC) - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016
http://www.wcci2016.org/

Conference

Conference2016 IEEE Congress on Evolutionary Computation (CEC)
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16
Internet address