Abstract
We proposed a new strategy to explicitly define introns that increases the probability of selecting good crossover points as evolution goes on. Our approach differs from existing methods in the procedure followed to adapt the probabilities of groups of code being protected. We also provide some experimental results in symbolic regression and classification that reinforced our belief in the usefulness of this procedure. Collateral effects of Evolutive Introns (EIs) are also studied to determine possible modifications in the behavior of a classical Genetic Programming (GP) system.
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Carbajal, S.G., Martinez, F.G. Evolutive Introns: A Non-Costly Method of Using Introns in GP. Genetic Programming and Evolvable Machines 2, 111–122 (2001). https://doi.org/10.1023/A:1011548229751
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DOI: https://doi.org/10.1023/A:1011548229751