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Titlebook: Genetic Programming; 12th European Confer Leonardo Vanneschi,Steven Gustafson,Marc Ebner Conference proceedings 2009 Springer-Verlag Berlin

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https://doi.org/10.1007/978-3-322-85269-4ach crossover event by its fitness properties and the information that contributes most strongly to those fitness properties. Some tentative evidence in support of the above hypothesis is extracted from this categorisation.
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https://doi.org/10.1007/978-3-322-80834-9tion of crossover and reproduction, and secondly using a combination of mutation and reproduction. We find that crossover does not significantly outperform mutation on most of the problems examined. In addition, we demonstrate that the use of a straightforward Design of Experiments methodology is effective at tuning GP algorithm parameters.
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Genetic Programming Based Approach for Synchronization with Parameter Mismatches in EEGting the CLM coupled equations to a very good accuracy with reasonable multi-step predictions. It is shown that synchronization in the above two systems is well understood in terms of parameter mismatches in the system equations derived by GP approach.
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Memory with Memory in Tree-Based Genetic Programmingviour in tree-based GP systems. The technique requires only very minor modifications to existing code, and, therefore, is easy to apply. Experiments on a variety of synthetic and real-world problems show that MwM is very beneficial in tree-based GP, too.
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Genetic Programming Crossover: Does It Cross over?ach crossover event by its fitness properties and the information that contributes most strongly to those fitness properties. Some tentative evidence in support of the above hypothesis is extracted from this categorisation.
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