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    neat Genetic Programming: Controlling bloat naturally


    Trujillo, Leonardo and Muñoz, Luis and Galván López, Edgar (2016) neat Genetic Programming: Controlling bloat naturally. Information Sciences, 333. pp. 21-43. ISSN 0020-0255

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    Abstract

    Bloat is one of the most widely studied phenomena in Genetic Programming (GP), it is normally defined as the increase in mean program size without a corresponding improvement in fitness. Several theories have been proposed in the specialized GP literature that explain why bloat occurs. In particular, the Crossover-Bias Theory states that the cause of bloat is that the distribution of program sizes during evolution is skewed in a way that encourages bloat to appear, by punishing small individuals and favoring larger ones. Therefore, several bloat control methods have been proposed that attempt to explicitly control the size distribution of programs within the evolving population. This work proposes a new bloat control method called neat-GP, that implicitly shapes the program size distribution during a GP run. neat-GP is based on two key elements: (a) the NeuroEvolution of Augmenting Topologies algorithm (NEAT), a robust heuristic that was originally developed to evolve neural networks; and (b) the Flat Operator Equalization bloat control method, that explicitly shapes the program size distributions toward a uniform or flat shape. Experimental results are encouraging in two domains, symbolic regression and classification of real-world data. neat-GP can curtail the effects of bloat without sacrificing performance, outperforming both standard GP and the Flat-OE method, without incurring in the computational overhead reported by some state-of-the-art bloat control methods.

    Item Type: Article
    Keywords: Genetic programming; Bloat; NeuroEvolution of augmenting topologies; Flat operator equalization;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 12330
    Identification Number: https://doi.org/10.1016/j.ins.2015.11.010
    Depositing User: Edgar Galvan
    Date Deposited: 31 Jan 2020 11:46
    Journal or Publication Title: Information Sciences
    Publisher: Elsevier
    Refereed: Yes
    URI:

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