A genetic algorithm framework for algorithmic design through logic circuit optimization

Abstract This paper presents the design of a logic circuit, whose topology is defined through an optimization process carried out by a genetic algorithm with the aim of obtaining an optimal tic-tac-toe player. To achieve this objective, the optimization process is performed by a simple genetic algorithm, and the problem is framed as a maximization problem. Given a logic circuit and a set of Boolean weights on its vertices, the goal is to optimize the performance of the logic circuit as a tic-tac-toe player by optimizing the weights that define its topology, where each set of weights defines a unique configuration and, therefore, a unique circuit. The genetic algorithm treats these sets of weights as individuals in its population. The design of the genetic algorithm involves evaluating various configurations of logic circuits, objective functions, and training models. The presented results showcase an optimal set of these configurations, as well as an analysis of the best player obtained.