Abstract
The traditional genetic algorithm in the search of complex nonlinear problems, prone to early convergence and the late search fatigue and other issues. In view of these problems, the paper proposes the micro variation chaos genetic algorithm. Using the characteristics of chaos and randomness, the genetic algorithm is used to compensate for the slow evolution of the genetic algorithm and the difference of the genetic diversity. At the same time, the crossover operator, mutation operator and fitness function of genetic algorithm are improved, and the performance is improved. The simulation results show that the micro variation chaotic adaptive genetic algorithm is faster and more accurate than the conventional chaos genetic algorithm and genetic algorithm.
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.
