Game Theory (GT) formalizes dispute scenarios between two or more players where each one makes a move following their strategy profiles. The following paper introduces the integration of GT to selection and crossover steps of Genetic Algorithms as an evolutionary model of the representation of population in a similar way to human social evolution. Two ideas are proposed to be incorporated into the GA. First, the Genetic Algorithm with Social Interaction (GASI), a family of GAs that uses GT in selection phase to increase the diversification of the solutions. Second, the (Game-Based Crossover) GBX and GBX2 crossover operators, competition-based tournament selection methods that employ social dispute to generate more diverse offspring. Performance and robustness of the new approaches were assessed by ten continuous and constrained engineering design optimization problems and compared against variants of the canonical GA, as well as well-known heuristics from the literature. Results indicate significant performance relevance in most instances compared to other algorithms and highlight the benefits of combining GT and GA.

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