Neutral Fitness Landscape in the Cellular Automata Majority Problem

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Verel, Sébastien
Collard, Philippe
Tomassini, Marco
Vanneschi, Leonardo
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We study in detail the fitness landscape of a difficult cellular automata computational task: the majority problem. Our results show why this problem landscape is so hard to search, and we quantify the large degree of neutrality found in various ways. We show that a particular subspace of the solution space, called the "Olympus", is where good solutions concentrate, and give measures to quantitatively characterize this subspace.
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Computer Science - Neural and Evolutionary Computing
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