On Enhancing Genetic Algorithms Using New Crossovers
This work addresses performance bottlenecks in genetic algorithms for optimization problems like TSP, but it is incremental as it builds on existing crossover methods.
The paper tackles the problem of improving genetic algorithms by proposing new crossover operators, including a Collision crossover based on elastic collision physics, and two selection strategies for operators. Results on Travelling Salesman Problems show significant performance enhancements, with the Collision crossover and multiple operators yielding notable improvements compared to Modified and PMX crossovers.
This paper investigates the use of more than one crossover operator to enhance the performance of genetic algorithms. Novel crossover operators are proposed such as the Collision crossover, which is based on the physical rules of elastic collision, in addition to proposing two selection strategies for the crossover operators, one of which is based on selecting the best crossover operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) have been conducted to evaluate the proposed methods, which are compared to the well-known Modified crossover operator and partially mapped Crossover (PMX) crossover. The results show the importance of some of the proposed methods, such as the collision crossover, in addition to the significant enhancement of the genetic algorithms performance, particularly when using more than one crossover operator.