Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Analysis of the Scenery Perceived by a Real Mobile Robot Khepera
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
CERMA '06 Proceedings of the Electronics, Robotics and Automotive Mechanics Conference - Volume 01
Exploring the T-Maze: evolving learning-like robot behaviors using CTRNNs
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An incremental approach to developing intelligent neural networkcontrollers for robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper explores a new approach of using a multi-objective evolutionary algorithm (MOEA) to evolve robot controllers in performing phototaxis task while avoiding obstacles or navigating through a maze in a simulated environment, to overcome problems involving more than one objective, where these objectives usually trade-off among each other and are expressed in different units. Experiments were conducted in six sets within a 10% noise environment with different task environment complexities to investigate whether the MOEA is effective for controller synthesis. A simulated Khepera robot is evolved by a Pareto-frontier Differential Evolution (PDE) algorithm, and learned through a 3-layer feed-forward artificial neural network, attempting to simultaneously fulfill two conflicting objectives of maximizing robot phototaxis behavior while minimizing the neural network's hidden neurons by generating a Pareto optimal set of controllers. Results showed that robot controllers could be successfully developed using the MOEA.