Less detectable environmental changes in dynamic multiobjective optimisation

Jiang, Shouyong and Kaiser, M. and Guo, J. and Yang, S. and Krasnogor, N. (2018) Less detectable environmental changes in dynamic multiobjective optimisation. GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference . pp. 673-680. ISSN UNSPECIFIED

Full content URL: https://doi.org/10.1145/3205455.3205521

Full text not available from this repository.

Item Type:Article
Item Status:Live Archive

Abstract

Multiobjective optimisation in dynamic environments is challenging due to the presence of dynamics in the problems in question. Whilst much progress has been made in benchmarks and algorithm design for dynamic multiobjective optimisation, there is a lack of work on the detectability of environmental changes and how this affects the performance of evolutionary algorithms. This is not intentionally left blank but due to the unavailability of suitable test cases to study. To bridge the gap, this work presents several scenarios where environmental changes are less likely to be detected. Our experimental studies suggest that the less detectable environments pose a big challenge to evolutionary algorithms. © 2018 Association for Computing Machinery.

Additional Information:cited By 0; Conference of 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 ; Conference Date: 15 July 2018 Through 19 July 2018; Conference Code:137822
Keywords:Benchmarking, Multiobjective optimization, Algorithm design, Detectability, Dynamic environments, Environmental change, Less detectable environment (LDE), Test case, Evolutionary algorithms
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:35660
Deposited On:17 Apr 2019 12:20

Repository Staff Only: item control page