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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CITATION STYLE
Albuquerque, P., Chopard, B., Mazza, C., & Tomassini, M. (2000). On the impact of the representation on fitness landscapes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1802, pp. 1–15). Springer Verlag. https://doi.org/10.1007/978-3-540-46239-2_1
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