A new multi-offspring crossover operator for genetic algorithm to facilitate the traveling salesman problem

Abstract

This research work provides a detailed working principle and official analysis of a multi-offspring crossover operator. The proposed operator explains the true theory of survival-of-fittest using the foundation of evolutionary theories of biology and ecological theories of mathematics. We found a considerable improvement because the proposed operator enhances the opportunity of having better offspring, which results in highly competitive population. Simulation results of this operator with other competitor crossover operators for one of the combinatorial optimization problems, i.e. traveling salesman problem, are obviously showing its pros at better accuracy level. Moreover, the t-test and performance index (PI) establishes the improved significance and accuracy levels of the proposed operator. Preferable results of this operator not only confirm its advantages over the others, but also show long run survival of a generation having a number of offspring more than the number of parents with the help of mathematical ecology theory.

Keywords

• traveling salesman problem
• genetic algorithms
• crossover operators
• multi-offspring
• performance index