Evolutionary Based on Selfish and Altruism Strategies - An Approach to Path Planning Problems

This paper presents a novel hybrid optimization approach based on a genetic algorithm that combines selfish gene and altruism view of evolution. The purpose of the present research is to develop a new optimization approach to solve path-planning problems, particularly to be used in robot trajectories planning. A brief discussion about selfish versus altruism is made in the perspective of genes, its integration in the chromosome (individuals) and the forces involved in the evolution process of genes, individuals and populations. The SAGA (Selfish-Altruist Genetic Algorithm) is the generalization of the Genetic Algorithms (GA), where the basic variables are the genes (characters or words) as non-autonomous entities, grouped in a Chromosome structure. The proposed hybrid approach was applied to a path-planning problem, in a continuous search space, to show its effectiveness in complex and interdependent sub-paths and evolution processes. Genes-centred evolution improved local sub-paths as sub-processes in a Chromosome-centred evolution and resulted in improved global planning trajectories when compared with standard genetic algorithm.