Towards Efficient Semantic Mutation in CGP: Enhancing SOMOk

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Efficient Semantic Mutation in CGP: Enhancing SOMOk

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Towards Efficient Semantic Mutation in CGP: Enhancing SOMOk

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA25-15490S" target="_blank" >GA25-15490S: LEDNeCo: Nízkoenergetické hluboké neurovýpočty</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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