Comparison of Metaheuristic Algorithms for Evolving a Neural Controller for an Autonomous Robot

Sergii Zhevzhyk, Wilfried Elmenreich

Abstract


Evolutionary algorithms are a possible way to automatically design the behavior of autonomous robots. In this paper we compare different evolutionary algorithms (EA), namely simple EA, two dimensional cellular EA, and random search, according to their performance in a simple simulation, where a phototaxis robot with two sensors of limited range has to find a light source in a closed area. In our experiments we studied the effects on performance of EA parameters, such as population size and number of generation. The results explain how the choice of the neural network (three-layered or fully-connected) may influence the quality of a final solution.

Our findings indicate that acceptable results can be achieved using all EAs but not with random search. The utilization of a fully-connected neural network allows achieving better results for all EAs as compared to a three-layered neural network. Two dimensional cellular EA and simple EA evolve the best strategies for a robot’s behavior which allow the robot to reach the light source in almost all cases.

Keywords


Evolutionary algorithm; Neural network; Robot simulation

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References


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DOI: http://dx.doi.org/10.14738/tmlai.26.783

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