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This article presents a parameter study on the applied game theory in Genetic Algorithm (GA), performing an analysis of the game Prisoner's Dilemma applied in the solution of four constrained Engineering problems. Simulations were applied in four different variations of the game, in order to find the best configuration for the analyzed problems. Then, after obtaining the best configuration of the game, we performed an analysis of the incidence of Alpha and Beta weights, present in GA fitness with Social Interaction, using the game that best suited each problem, employing sixteen different weight configurations. At end, the results obtained authenticated the influence of the Social Interaction process in the essence of GA.

The following paper demonstrates the possibilities of adapting the Ant Colony Algorithm with Social Interaction coming from Game Theory. This novel algorithm, named Social Interaction Ant Colony Optimization (SIACO), were based on the Ant System Algorithm developed by Dorigo and Social Interaction created by Otavio Teixeira in Genetic Algorithm. A new phase was inserted in the Ant System and the game is performed by two ants inside the colony. Four instances of Travelling Salesman Problem (TSP) were used to validate the approach and its results shows that the proposed can be a rival of other algorithms when applied to this class of problems.

This paper has the purpose to show game theory (GT) applied to genetic algorithms (GA) as a new type of interaction between individuals of GA. The game theory increases the exploration potential of the genetic algorithm by changing the fitness with social interaction between individuals, avoiding the algorithm to fall in a local optimum. To increase the exploitation potential of this approach, this work will present the adjusted crossover operator and compare results to other crossover methods.

The following paper introduces a parallel approach to a social variant of the Genetic Algorithm, called Parallel Genetic Algorithm with Social Interaction (PSIGA). The algorithm is based on social games involving game theory, and it is implemented using the OpenMP API, which is based on the shared memory programming model for multiple processor architectures. The main contribution of this approach is the parallelization using the Shared Memory of the Social Interaction Genetic Algorithm (SIGA) in order to achieve faster and better optimality than its nonparallel counterpart for global optimization problems with restrictions. For means of performance assessment, the algorithm is tested on four instances of engineering design problems and the obtained results compared with the Genetic Algorithm with Social Interaction (SIGA) implemented in sequential programming model.

This paper has the purpose to present a new hybrid nature inspired metaheuristic developed based on three fundamentals pillars extremely well known: Genetic Algorithms, Game Theory and Fuzzy Systems. This new approach tries to mimic a little bit more closer how a population of individuals evolves along time, like human social evolution emphasizing the social interaction between individuals and the non-binary behavior of human decision making against the classical cooperate-defect behavior present in the Prisoner's Dilemma (PD), for example. In this way it is presented the Social Interaction Genetic Algorithm (SIGA), to establish the necessary basis for the application of fuzzy concepts to get the F-SIGA Algorithm. Besides that, it is also presented the structure of an individual more complex with a genotype composed of two chromosomes, one for the solution of the problem and the other representing its behavior's strategy, which could be binary or fuzzy. At least the F-SIGA approach is presented in details, including all its steps. And finally some results are presented to an instance of the Traveling Salesman Problem.

This work has the purpose to present a new hybrid metaheuristic developed based on three fundamentals pillars extremely well known: Genetic Algorithms, Game Theory and Fuzzy Systems. This new approach tries to mimic a little bit more closer how a population of individuals evolves along time, like human social evolution emphasizing the social interaction between individuals and the non-binary behavior of human decision making against the classical cooperate-defect behavior present in the Prisoner's Dilemma. In this way it is also presented the SIGA Algorithm [9], the approach of an individual more complex with a genotype composed of two chromosomes, one for the solution of the problem and the other representing its strategy, a binary or fuzzy. Finally some results are presented to an instance of the Traveling Salesman Problem.

In this paper, it is presented a new way to characterize the phenotype in the context of Genetic Algorithms through the use of Game Theory as a theoretical foundation to define a new phase in the algorithm, named Social Interaction. It is executed before the reproduction phase and allows individuals to fight for their own survival improving their fitness according to the rules of a game. Thereby, a new algorithm is presented and some good results were produced for Traveling Salesman Problem an improvement in Genetic Algorithm execution.