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Towards Efficient Semantic Mutation in CGP: Enhancing SOMOk

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0197538" target="_blank" >RIV/00216305:26230/26:0197538 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1145/3712255.3734289" target="_blank" >http://dx.doi.org/10.1145/3712255.3734289</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3712255.3734289" target="_blank" >10.1145/3712255.3734289</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Towards Efficient Semantic Mutation in CGP: Enhancing SOMOk

  • Original language description

    Genetic Programming (GP) and its variants have proven to be promising techniques for solving problems across various domains. However, GP does not scale well, particularly when applied to symbolic regression in the Boolean domain. To address this limitation, a semantically oriented mutation operator (SOMO) has been proposed and integrated with Cartesian Genetic Programming (CGP). Nevertheless, like standard GP, even SOMO suffers in some cases from bloat - an excessive growth in solution size without a corresponding performance gain. This work introduces SOMOk-TS, an extension of SOMO that incorporates the so-called Tumor Search strategy to identify and preserve reusable substructures. By managing diversity through an immune-inspired mechanism, SOMOk-TS promotes the reuse of substructures, thereby reducing computational overhead. It achieves significantly lower execution times while maintaining or improving solution compactness, highlighting its potential for scalable and efficient evolutionary design.

  • Czech name

  • Czech description

Classification

  • Type

    O - Miscellaneous

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA25-15490S" target="_blank" >GA25-15490S: LEDNeCo: Low Energy Deep Neurocomputing</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2025

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů