PEEC: Evolving Efficient Connections Using Pareto Optimality


Pareto optimality is a criteria of individual evaluation originally introduced in multi-objective evolutionary algorithms. In the last decade, a growing interest in the integration of Pareto optimality and other evolutionary techniques can be observed. In this work, we integrate EEC, a neuroevolutionary (NE) algorithm, with Pareto optimality. The proposed algorithm is called PEEC. We demonstrate the algorithm on a classic board game, Tic-Tac-Toe, and compare its performance with EEC using three other evaluation models. Our experimental results show that PEEC outperforms all of these and Pareto optimality indeed provides more accurate evaluation to guide NE toward optimal solutions.