The Influence of Three Parent Crossbreeding on the Dual Population Genetic Algortihm

A genetic algorithm (GA) is an optimization technique based on natural genetics, using selection, crossover, and mutation. Crossover combines genetic material from two parents to create offspring, maintaining diversity and preventing premature convergence. While the two parents are typically used, multi-parent crossover, involving more than two parents, has shown superior results. in this paper is explored the multi-parent crossover in dual genetic algorithms, which facilitate information exchange between populations through interpolation crossbreeding. Offspring inherit traits from both parent populations, improving adaptability. The Cave-Surface GA (CSGA) with three-parent crossover is tested on 15 Travelling Salesman Problem (TSP) benchmarks. Results show that the CSGA outperforms both traditional GAs and two-parent CSGA. This method demonstrates great potential for complex optimization challenges.