GP-RARS: evolving controllers for the Robot Auto Racing Simulator

In Genetic Programming (GP), most often the search space grows in a greater than linear fashion as the number of tasks required to be accomplished increases. This is a cause for one of the greatest problems in Evolutionary Computation (EC): scalability. The aim of the work presented here is to facilitate the evolution of control systems for complex robotic systems. The authors use a combination of mechanisms specifically designed to facilitate the fast evolution of systems with multiple objectives. These mechanisms are: a genetic transposition inspired seeding, a strongly-typed crossover, and a multiobjective optimization. The authors demonstrate that, when used together, these mechanisms not only improve the performance of GP but also the reliability of the final designs. They investigate the effect of the aforementioned mechanisms on the efficiency of GP employed for the coevolution of locomotion gaits and sensing of a simulated snake-like robot (Snakebot). Experimental results show that the mechanisms set forth contribute to significant increase in the efficiency of the evolution of fast moving and sensing Snakebots as well as the robustness of the final designs.

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GP-RARS: evolving controllers for the Robot Auto Racing Simulator

Cited by 3 publications

References 17 publications

Incremental evolution of fast moving and sensing simulated snake-like robot with multiobjective GP and strongly-typed crossover

Incremental evolution of fast moving and sensing simulated snake-like robot with multiobjective GP and strongly-typed crossover

HICMA: A human imitating cognitive modeling agent using statistical methods and evolutionary computation

Efficient Evolution of Modular Robot Control via Genetic Programming

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