Less detectable environmental changes in dynamic multiobjective optimisation
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.
Citation : Jiang, S., Kaiser, M., Guo, J., Yang, S. and N. Krasnogor. N. (2018) Less detectable environmental changes in dynamic multiobjective optimisation. Proceedings of the 2018 Genetic and Evolutionary Computation Conference, 2018
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes