On-the-fly simplification of genetic programming models

Javed, Noman; Gobet, Fernand

doi:10.1145/3412841.3441926

Cited by 5 publications

(7 citation statements)

References 19 publications

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“…Every generation [2], [117]- [122] Final generation [2], [123], [124] At certain interval [119], [125]- [128] Which individuals All individuals in the population [2], [119], [122], [125]- [127] The best individuals in the population [2], [121], [123], [124], [128] Randomly with some probability [117] The parents for breeding [118], [120] How Genotypic (Structural) [2], [117]- [119], [122], [123],…”

Section: Whenmentioning

confidence: 99%

“…For example, if there is always little difference between the output of a sub-tree and that of its child, then the sub-tree can be replaced by that child [120], [126], [127]. Permutation test is also commonly used to estimate the contribution of a sub-tree (e.g., [124], [128], [137]). If the overall model output is not affected by shuffling the output of a sub-tree, then the sub-tree can be pruned.…”

Section: Whenmentioning

confidence: 99%

See 1 more Smart Citation

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%

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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“…To use AI systems for scientific discovery, outputs must be understandable to humans and allow interpretation in the context of current theories and knowledge ( Javed & Gobet, 2021 ). A key issue is that, although the inputs and outputs of an AI system are known, the intermediate steps are sometimes difficult to understand (it is a “black box”), which can affect the interpretability of the output data.…”

Section: Computational Scientific Discovery: a Critical Evaluationmentioning

confidence: 99%

Computational Scientific Discovery in Psychology

Bartlett

Pirrone

Javed

et al. 2022

Perspect Psychol Sci

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Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.

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“…Another option is to "NOOP" a gene, that is to replace it with a NOOP instruction which, upon execution, will do nothing. The authors of the paper proposed pruning as an operator (Javed and Gobet 2021). The idea was to split a parent into its children and replace it with the child having the highest fitness.…”

Section: Fitness-based Simplificationmentioning

confidence: 99%

“…Rather than working on single individuals, it tries to simplify the whole population at once. The authors of this paper proposed generation-wide simplification (Gws) (Javed and Gobet 2021). In Gws, after every k th generation, the new population is created by rupturing the individuals rather than by using the routine genetic operators of crossover and mutation.…”

Section: Population-level 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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On-the-fly simplification of genetic programming models

Cited by 5 publications

References 19 publications

Explainable Artificial Intelligence by Genetic Programming: A Survey

Explainable Artificial Intelligence by Genetic Programming: A Survey

Computational Scientific Discovery in Psychology

Simplification of genetic programs: a literature survey

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