Improving Computational Performance of Genetic Algorithms: A Comparison of Techniques

A comparison of three methods for saving previously calculated fitness values across generations of a genetic algorithm is made. These methods lead to significant computational performance improvements. For real world problems, the computational effort spent on evaluating the fitness function far exceeds that of the genetic operators. As the population evolves, diversity usually diminishes. This causes the same chromosomes to be frequently reevaluated. By using appropriate data structures to store the evaluated fitness values of chromosomes, significant performance improvements are realized. Several different data structures are compared and contrasted. This paper shows that, for different sets of genetic algorithm parameters, including selection type, population size, and level of mutation, performance improvements are realized.