Autonomous Agents with Control Systems Based on Genetic Algorithms

S. Goschin, E. Franti, M. Dascalu, and M. Pietraroiu (Romania)


Evolutionary robotics, simulation, neural networks and genetic algorithms


The purpose of this paper is to present a method of combining neural networks and genetic algorithms to create an efficient control system for a simulated autonomous robot in a 3D environment. The experiments concern the emergence of behaviours like obstacle avoidance, target hitting and shortest path finding using a simple yet robust architecture. The evolutionary process takes place inside a dedicated, flexible framework that allows further development and testing.

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