Abstract
A new crossover technique for genetic algorithms is proposed in this paper. The technique is called probabilistic adaptive crossover and denoted by PAX. The method includes the estimation of the probability distribution of the population, in order to store in a unique probability vector P information about the best and the worse solutions of the problem to be solved. The proposed methodology is compared with six crossover techniques namely: one-point crossover, two-point crossover, SANUX, discrete crossover, uniform crossover and selective crossover. These methodologies were simulated and compared over five test problems described by ONEMAX Function, Royal Road Function, Random L-MaxSAT, Bohachevsky Function, and the Himmelblau Function.
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 callMore From: International Journal on Artificial Intelligence Tools
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.
