Abstract

Evolutionary algorithms often suffer from premature convergence when dealing with complex multi-modal function optimization problems as the fitness landscape may contain numerous local optima. To avoid premature convergence, sufficient amount of genetic diversity within the evolving population needs to be preserved. In this paper we investigate the impact of two different categories of mutation operators on evolutionary programming in an attempt to preserve genetic diversity. Participation of the mutation operators on the evolutionary process is guided by fitness stagnation and localization information of the individuals. A simple experimental analysis has been shown to demonstrate the effectiveness of the proposed scheme over a test-suite of five classical benchmark functions

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 call

Disclaimer: 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.