Abstract
Virus evolutionary genetic algorithm (VEGA) is an improved genetic algorithm (GA) that can prevent premature convergence, which introduces an additional virus population and two infection operators to GA. In this paper, the optimization mechanism of the binary-coding VEGA is analyzed. By the geometrical representation of the virus individual, the virus reverse transcription operations is transformed to be equivalent to the crossover among several host individuals in several different generations. As these host individuals may be close to the best solution of the target problem, VEGA's effectiveness is theoretical deterministic.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.