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<p>The Semantic Learning algorithm based on Inflate and deflate Mutation&#xa0;(SLIM) represents a recent advancement in Geometric Semantic Genetic Programming&#xa0;(GSGP). While maintaining the unique feature of generating a unimodal error surface for all supervised learning tasks, SLIM has the ability of generating models that remain sufficiently compact for human interpretation. In its initial definition, SLIM was characterized by two mutation operators: the traditional geometric semantic mutation, called inflate mutation because it produces offspring larger than their parents, and a new deflate mutation that has the ability of creating smaller offspring, offering a novel approach to managing model complexity. Besides deepening and interpreting in greater depth the experimental results, this work further enriches the foundational concepts of SLIM by integrating a new crossover operator, that, contrarily to the traditional geometric semantic crossover, is able to generate individuals of small size. Our comprehensive analysis explores the wider implications of this innovative operator. The novel variant that integrates this crossover is named Semantic Learning algorithm with Inflate/deflate Mutations and MEaningful Recombination&#xa0;(SLIMMER). Experimental results provide strong support for the potential of both&#xa0;SLIM and&#xa0;SLIMMER as effective approaches worthy of further research. As its name suggests, for some test cases&#xa0;SLIMMER demonstrates an enhanced ability to produce even more compact models than&#xa0;SLIM, further reinforcing its promise for applications where interpretability and model simplicity are essential.</p>

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SLIM: the non-bloating genetic programming with geometric semantic mutations and meaningful semantic crossover

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Leonardo Vanneschi,
  • Sofia Pereira,
  • Davide Farinati

摘要

The Semantic Learning algorithm based on Inflate and deflate Mutation (SLIM) represents a recent advancement in Geometric Semantic Genetic Programming (GSGP). While maintaining the unique feature of generating a unimodal error surface for all supervised learning tasks, SLIM has the ability of generating models that remain sufficiently compact for human interpretation. In its initial definition, SLIM was characterized by two mutation operators: the traditional geometric semantic mutation, called inflate mutation because it produces offspring larger than their parents, and a new deflate mutation that has the ability of creating smaller offspring, offering a novel approach to managing model complexity. Besides deepening and interpreting in greater depth the experimental results, this work further enriches the foundational concepts of SLIM by integrating a new crossover operator, that, contrarily to the traditional geometric semantic crossover, is able to generate individuals of small size. Our comprehensive analysis explores the wider implications of this innovative operator. The novel variant that integrates this crossover is named Semantic Learning algorithm with Inflate/deflate Mutations and MEaningful Recombination (SLIMMER). Experimental results provide strong support for the potential of both SLIM and SLIMMER as effective approaches worthy of further research. As its name suggests, for some test cases SLIMMER demonstrates an enhanced ability to produce even more compact models than SLIM, further reinforcing its promise for applications where interpretability and model simplicity are essential.