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        Simplification of genetic programs: A literature survey

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        Author
        Javed, Noman
        Gobet, Fernand
        Lane, Peter
        Attention
        2299/25499
        Abstract
        Genetic programming (GP), a widely used Evolutionary Computing technique, suffers from bloat -- the problem of excessive growth in individuals' sizes. As a result, its ability to efficiently explore complex search spaces reduces. The resulting solutions are less robust and generalisable. Moreover, it is difficult to understand and explain models which contain bloat. This phenomenon is well researched, primarily from the angle of controlling bloat: instead, our focus in this paper is to review the literature from an explainability point of view, by looking at how simplification can make GP models more explainable by reducing their sizes. Simplification is a code editing technique whose primary purpose is to make GP models more explainable. However, it can offer bloat control as an additional benefit when implemented and applied with caution. Researchers have proposed several simplification techniques and adopted various strategies to implement them. We organise the literature along multiple axes to identify the relative strengths and weaknesses of simplification techniques and to identify emerging trends and areas for future exploration. We highlight design and integration challenges and propose several avenues for research. One of them is to consider simplification as a standalone operator, rather than an extension of the standard crossover or mutation operators. Its role is then more clearly complementary to other GP operators, and it can be integrated as an optional feature into an existing GP setup. Another proposed avenue is to explore the lack of utilisation of complexity measures in simplification. So far, size is the most discussed measure, with only two pieces of prior work pointing out the benefits of using time as a measure when controlling bloat.
        Publication date
        2022-04-27
        Published in
        Data Mining and Knowledge Discovery
        Published version
        https://doi.org/10.1007/s10618-022-00830-7
        License
        http://creativecommons.org/licenses/by/4.0/
        Other links
        http://hdl.handle.net/2299/25499
        Relations
        School of Physics, Engineering & Computer Science
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