Abstract
In this paper we study the role of program representation on the properties of a type of Genetic Programming (GP) algorithm. In a specific case, which we believe to be generic of standard GP, we show that the way individuals are coded is an essential concept which impacts the fitness landscape. We give evidence that the ruggedness of the landscape affects the behavior of the algorithm and we find that, below a critical population, whose size is representation-dependent, premature convergence occurs.
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Albuquerque, P., Chopard, B., Mazza, C., Tomassini, M. (2000). On the Impact of the Representation on Fitness Landscapes. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds) Genetic Programming. EuroGP 2000. Lecture Notes in Computer Science, vol 1802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46239-2_1
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DOI: https://doi.org/10.1007/978-3-540-46239-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67339-2
Online ISBN: 978-3-540-46239-2
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