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In recent years, semantics-based crossover operators have attracted attention for efficient search in Genetic Programming (GP). Geometric Semantic Genetic Programming (GSGP) is one of the methods, in which a convex combination of two parents is used for creating an offspring. We have previously proposed an improved GSGP, Deterministic GSGP. In Deterministic GSGP, the convex combination is relaxed...
In this paper, we focus on evolutionary optimization of multi-agent behavior. There are two representative models for multi-agent control, homogeneous and heterogeneous models. In the homogeneous model, all agents are controlled by the same controller. Therefore, it is difficult to realize complex cooperative behavior such as division of labors. In contrast, in the heterogeneous model, respective...
Ant Programming (AP) is an automatic programming method, which combines tree-structural representations of Genetic Programming (GP) and search mechanism by pheromone communications of ants in Ant Colony Optimization (ACO). In AP, a single prototype tree, in which respective nodes have different pheromone tables, is prepared, and an ant searches solutions under the prototype tree. The structure of...
Genetic Programming (GP) is an evolutionary method for generating tree structural programs. Normal subtree crossover in GP randomly selects a crossover point in each parental tree, and offspring are created by exchanging the selected subtrees. In the normal crossover, it is difficult to control the global and local search because the similarity between the subtrees is not considered. In this paper,...
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