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.
|Title of host publication||GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||8|
|Publication status||Published - 2 Jul 2018|
|Event||2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan|
Duration: 15 Jul 2018 → 19 Jul 2018
|Name||GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference|
|Conference||2018 Genetic and Evolutionary Computation Conference, GECCO 2018|
|Period||15/07/18 → 19/07/18|
Bibliographical noteFunding Information:
SJ, MK, and NK acknowledge the EPSRC for funding project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)”.
© 2018 Association for Computing Machinery.
- Dynamic multiobjective optimisation
- Environmental changes
- Less detectable environment (LDE)