Abstract
Abstract. In this paper new evolutionary method of solving multi-objective optimization problems is presented. This method utilizes an information about an individual sex for the purpose of distinction between different groups of objectives. In particular, this information is extracted from the fitness of individuals and applied during the parental crossovers in a multi-objective optimization process. Characteristics of this mechanism are discussed and its performance in an exemplary multi-objective PID control optimization task is presented.
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.
