Simplification of genetic programs: a literature survey

Created by W.Langdon from gp-bibliography.bib Revision:1.9225

@Article{Javed:2022:DMKD,

• author = "Noman Javed and Fernand Gobet and Peter Lane",
• title = "Simplification of genetic programs: a literature survey",
• journal = "Data Mining and Knowledge Discovery",
• year = "2022",
• volume = "36",
• number = "4",
• pages = "1279--1300",
• month = jul,
• note = "Special Issue on Explainable and Interpretable Machine Learning and Data Mining",
• keywords = "genetic algorithms, genetic programming, Simplification, Bloat control, Explainability, XAI, Genetically Evolving Models in Science, GEMS",
• ISSN = "1384-5810",
• URL = "http://eprints.lse.ac.uk/114852/",
• URL = "https://uhra.herts.ac.uk/id/eprint/9655/",
• URL = "https://rdcu.be/cUozw",
• DOI = "10.1007/s10618-022-00830-7",
• size = "22 pages",
• 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 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.",
• notes = "CPNSS, London School of Economics and Political Science, London, UK",

}

Genetic Programming entries for Noman Javed Fernand Gobet Peter C R Lane

Citations