Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization

The aim of this paper is to identify Genetic Algorithms (GAs) which perform well over a range of continuous and smooth multimodal real-variable functions. In our study, we focus on testing GAs combining three classes of genetic operators: selection, crossover and replacement. The approach followed is time-constrained and thus our stopping criterion is a fixed number of generations. Results show that GAs with random selection of parents and crowding replacement are robust optimizers. By contrast, GAs with tournament selection of parents and random replacement perform poorly in comparison.