Summary
We describe a framework for program evolution with an EDA-based approach. In this framework, the probability distribution of programs is estimated using a Bayesian network, and individuals are generated based on the estimated distribution. Considering that a dependency relationship of nodes in a program tree is explicit, i.e. the dependency relationship is strong between a parent node and its child node in a program expressed as a tree structure, we have chosen a Bayesian network as the distribution model of programs.
In order to demonstrate the effectiveness of our approach, this chapter shows results of comparative experiments with Genetic Programming. Thereafter, we discuss how Estimation of Distribution Programming works and the transitions of the evolved programs that are the forte of our methods. We also analyze the performance of a hybrid system which combines Estimation of Distribution Programming and Genetic Programming.
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Yanai, K., Iba, H. (2006). Estimation of Distribution Programming: EDA-based Approach to Program Generation. In: Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds) Towards a New Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32494-1_5
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