Abstract
AbstractRecent research on evolutionary algorithms has begun to focus on the issue of generalization. While most works emphasize the evolution of high quality solutions for particular problem instances, others are addressing the issue of evolving solutions that can generalize in different scenarios, which is also the focus of the present paper. In particular, this paper compares fitness-based search, Novelty Search (NS), and random search in a set of generalization oriented experiments in a maze navigation problem using Grammatical Evolution (GE), a variant of Genetic Programming. Experimental results suggest that NS outperforms the other search methods in terms of evolving general navigation behaviors that are able to cope with different initial conditions within a static deceptive maze.KeywordsNovelty SearchGrammatical EvolutionGenetic Programming
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