Abstract
From several years, genetic algorithms are being used for solving many types of constrained and unconstrained optimization problems. Type of crossover operator, rate of crossover, replacement scheme and mutation rate are some of the essential factors that contribute to the efficiency of GA. Evidently, not one algorithm is suited for every problem according to the free lunch theorem but one form of crossover can solve a large number of problems. So many variants of GA are proposed in the past years with different types of crossover operators. So in this paper, we are introducing a new modified GA with improved crossover by dynamically choosing the type of crossover operator which is going to be used for the problem. Randomized mutation is used and a new virtual population is created which contains the best population so far. The modified GA is tested on 40 benchmark functions and the results are compared with the basic GA and one other GA variant.
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.
