Parameter control using the agent based patchwork model

The setting of parameters in Evolutionary Algorithms (EA) has crucial influence on their performance. Typically, the best choice depends on the optimization task. Some parameters yield better results when they are varied during the run. Recently, the so-called Terrain-Based Genetic Algorithm (TBGA) was introduced, which is a self-tuning version of the traditional Cellular Genetic Algorithm (CGA). In a TBGA, the individuals of the population are placed in a two-dimensional grid, where only neighbored individuals can mate with each other. The position of an individual in this grid is interpreted as its offspring´s specific mutation rate and number of crossover points. This approach allows to apply GA parameters that are optimal for (i) the type of optimization task and (ii) the current state of the optimization process. However, only a few individuals can apply the optimal parameters simultaneously due to their fixed position in the grid lattice. In this paper, we substituted the fixed spatial structure of CGAs with the agent-based Patchwork model. In this model individuals can move between neighbored grid cells, and the number of individuals per grid cell is variable but limited. With this design, several individuals were able to use beneficial parameters simultaneously and to follow optimal parameter settings over time. Our new approach achieved better results than our original Patchwork model and the TBGA