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Lookup NU author(s): Dr Shouyong Jiang, Professor Marcus Kaiser, Professor Natalio KrasnogorORCiD
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© 2018 Association for Computing Machinery. 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.
Author(s): Jiang S, Kaiser M, Guo J, Yang S, Krasnogor N
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
Year of Conference: 2018
Pages: 673-680
Acceptance date: 15/07/2018
Publisher: Association for Computing Machinery, Inc
URL: https://doi.org/10.1145/3205455.3205521
DOI: 10.1145/3205455.3205521
Library holdings: Search Newcastle University Library for this item
ISBN: 9781450356183
