TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0197539" target="_blank" >RIV/00216305:26230/26:0197539 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1145/3712255.3726697" target="_blank" >http://dx.doi.org/10.1145/3712255.3726697</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3712255.3726697" target="_blank" >10.1145/3712255.3726697</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
Popis výsledku v původním jazyce
Over the years, genetic programming (GP) has evolved, with many proposed variations, especially in how they represent a solution. Being essentially a program synthesis algorithm, it is capable of tackling multiple problem domains. Current benchmarking initiatives are fragmented, as the different representations are not compared with each other and their performance is not measured across the different domains. In this work, we propose a unified framework, dubbed TinyverseGP (inspired by tinyGP), which provides support to multiple representations and problem domains, including symbolic regression, logic synthesis and policy search.
Název v anglickém jazyce
TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
Popis výsledku anglicky
Over the years, genetic programming (GP) has evolved, with many proposed variations, especially in how they represent a solution. Being essentially a program synthesis algorithm, it is capable of tackling multiple problem domains. Current benchmarking initiatives are fragmented, as the different representations are not compared with each other and their performance is not measured across the different domains. In this work, we propose a unified framework, dubbed TinyverseGP (inspired by tinyGP), which provides support to multiple representations and problem domains, including symbolic regression, logic synthesis and policy search.
Klasifikace
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)
Návaznosti výsledku
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)
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů