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Titlebook: Genetic Programming; 17th European Confer Miguel Nicolau,Krzysztof Krawiec,Kevin Sim Conference proceedings 2014 Springer-Verlag Berlin Hei

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Semantic Crossover Based on the Partial Derivative Errorhrough the tree. This process decides the crossing point of the second parent. The results show that our procedure improves the performance of genetic programming on rational symbolic regression problems.
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The Best Things Don’t Always Come in Small Packages: Constant Creation in Grammatical Evolutioner..The results are surprising. The GE methods all perform significantly better than GP on unseen test data, and we demonstrate that the standard GE approach of . does not produce individuals that are any larger than those from methods which are designed to use less genetic material.
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Informationen und Informationsmodelle,istance measure. This paper investigates the use of Genetic Programming for the evolution of task-specific distance measures as an alternative to Euclidean distance. Results on seven real-world datasets show that the generalisation performance of the proposed system is superior to that of Euclidean-based kernel regression and standard GP.
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CGP does not suffer from bloat. It has also been shown for CGP that using a very large number of nodes considerably increases the effectiveness of the search. This paper also proposes a new explanation as to why this may be the case.
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