Abstract
The representation of a problem for evolutionary computation is the choice of the data structure used for solutions and the variation operators that act upon that data structure. For a difficult problem, choosing a good representation can have an enormous impact on the performance of the evolutionary computation system. To understand why this is so, one must consider the search space and the fitness landscape induced by the representation. If someone speaks of the fitness landscape of a problem, they have committed a logical error: problems do not have a fitness landscape. The data structure used to represent solutions for a problem in an evolutionary algorithm establishes the set of points in the search space. The topology or connectivity that joins those points is induced by the variation operators, usually crossover and mutation. Points are connected if they differ by one application of the variation operators. Assigning fitness values to each point makes this a fitness landscape. The question of the type of fitness landscape created when a representation is chosen is a very difficult one, and we will explore it in this chapter.
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Ashlock, D., McGuinness, C., Ashlock, W. (2012). Representation in Evolutionary Computation. In: Liu, J., Alippi, C., Bouchon-Meunier, B., Greenwood, G.W., Abbass, H.A. (eds) Advances in Computational Intelligence. WCCI 2012. Lecture Notes in Computer Science, vol 7311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30687-7_5
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