Abstract
In evolutionary optimisation, the preselection aims to choose promising solutions from a set of candidates for the fitness evaluation. It is usually based on the approximated fitness values, which are not necessary in many cases because we are usually interested in whether a candidate is promising or not instead of how promising it is. Actually, the preselection can be regarded as a classification process, i.e., to assign each candidate solution a label (+1 if promising or –1 otherwise). To this end, this paper proposes a classification based preselection (CPS) strategy and applies it to evolutionary optimisation. Systematic experiments are conducted to study the performance of CPS and the experimental results suggest that it can significantly improve the performance of some state-of-the-art evolutionary algorithms on most of the given test instances.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
