Evolutionary Algorithms: Multimodal Problems and Spatial Distribution

TL;DR: A detailed study of the techniques of solving multimodal problems by using spatial evolutionary algorithms is done and the design details of new mechanisms that allow us to reallocate the space of solutions are introduced, so that the resolution of complex problems with multiple local or global solutions can be solved.

Abstract: Over the last few decades, optimization problems have gained special attention in the world of computing, mainly because thanks to them, complex problems, which could only be addressed through approaches, now can be solved. In the wild, biodiversity is manifested by subtle differences in the individuals genetic code and consequently in the evolution of species. This approach is intended to apply to solving optimization problems through multimodal evolutionary algorithms. Standard evolutionary algorithms are not able to find more than a local optimum in the case of multimodal functions due to stochastic errors are committed (an individual randomly move one class to another) and that the population has a finite size (finite diversity). For this reason, in this work, a detailed study of the techniques of solving multimodal problems by using spatial evolutionary algorithms is done. In addition, the design details of new mechanisms for spatial evolutionary algorithms that allow us to reallocate the space of solutions are introduced. Thus, we will be able to deal with the resolution of complex problems with multiple local or global solutions.