Preselection via one-class classification for evolutionary optimization
Jinyuan Zhang, Aimin Zhou, Guixu Zhang · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018
In evolutionary algorithms, a preselection operator aims to choose some promising offspring solutions for a further environmental selection. Most existing preselection operators are based on fitness values, surrogate models, or classification models. Since a preselection operation can be regarded as a classification procedure, the classification based preselection is a natural choice for evolutionary algorithms. However, it is not trivial to prepare 'positive' and 'negative' training samples by using binary and/or multi-class classification models in preselection. To deal with this problem, this paper proposes a one-class classification based preselection (OCPS) scheme, which only needs one class of 'positive' training samples. The proposed OCPS scheme is applied to two state-of-the-art evolutionary algorithms on a test suite. The experimental results show the potential of OCPS on improving the performance of some existing evolutionary algorithms.