Fitness Functions in NEAT-Evolved Maze Solving Robots
نویسندگان
چکیده
In this paper, we examine the effect of fitness functions on the ability of a robot evolved with NEAT (NeuroEvolution of Augmenting Topologies) to find a light in a simple maze. By varying the fitness function used to determine a genotype’s likelihood of persisting in the next generation, we propose to look at how a robot’s solution to a task is influenced by the prevalence of intermediate and outcomerelated rewards in its fitness score. We also consider the effects of combining multiple rewards, with an emphasis on how they are weighted.
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