Multi-objective Co-operative Co-evolutionary Genetic Algorithm
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
On identifying global optima in cooperative coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Ideal Evaluation from Coevolution
Evolutionary Computation
Coevolution of neural networks using a layered pareto archive
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hierarchical cooperative coevolution facilitates the redesign of agent-based systems
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
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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.