Reducing code bloat in Genetic Programming based on subtree substituting technique

Chu, Thi Huong; Nguyen, Quang Uy

doi:10.1109/iesys.2017.8233556

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…An intuitive approach arbitrarily changes the model to make it smaller. If the model behaviour is not significantly changed, then the original model is replaced by the smaller one [132], [134], [135]. Also, a part of the model can be pruned if it is never executed during the model evaluation [136].…”

Section: Whenmentioning

confidence: 99%

Explainable Artificial Intelligence by Genetic Programming: A Survey

Mei

Chen

Lensen

et al. 2023

IEEE Trans. Evol. Computat.

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Explainable artificial intelligence has received great interest in the recent decade, due to its importance in critical application domains such as self-driving cars, law and healthcare. Genetic programming is a powerful evolutionary algorithm for machine learning. Compared with other standard machine learning models such as neural networks, the models evolved by GP tend to be more interpretable due to their model structure with symbolic components. However, interpretability has not been explicitly considered in genetic programming until recently, following the surge in popularity of explainable artificial intelligence. This paper provides a comprehensive review of the studies on genetic programming that can potentially improve the model interpretability, both explicitly and implicitly, as a byproduct. We group the existing studies related to explainable artificial intelligence by genetic programming into two categories. The first category considers the intrinsic interpretability, aiming to directly evolve more interpretable (and effective) models by genetic programming. The second category focuses on posthoc interpretability, which uses genetic programming to explain other black-box machine learning models, or explain the models evolved by genetic programming by simpler models such as linear models. This comprehensive survey demonstrates the strong potential of genetic programming for improving the interpretability of machine learning models and balancing the complex trade-off between model accuracy and interpretability.

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Section: Whenmentioning

confidence: 99%

Explainable Artificial Intelligence by Genetic Programming: A Survey

Mei

Chen

Lensen

et al. 2023

IEEE Trans. Evol. Computat.

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“…The semantics equivalence is calculated based on the similarity in the output vector on certain regression points. Chu and Nguyen (2017) propose to replace a randomly selected subtree with a terminal of approximate semantics. They did that by growing a tree, using the selection terminal, of approximate semantics.…”

Section: Semantics-based Simplificationmentioning

confidence: 99%

Simplification of genetic programs: a literature survey

Javed

Gobet

Lane

2022

Data Min Knowl Disc

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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.

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“…The main limitation of this approach is the penalty incurred in the form of computational cost. The authors of [4] have used a very similar technique of replacing a subtree with a terminal. They further extended their approach and proposed to replace a subtree with another subtree with approximately the same semantics [5,15].…”

Section: Simplificationmentioning

confidence: 99%