{"title":"On the hybridization of geometric semantic GP with gradient-based optimizers","authors":"Gloria Pietropolli, Luca Manzoni, Alessia Paoletti, Mauro Castelli","doi":"10.1007/s10710-023-09463-1","DOIUrl":null,"url":null,"abstract":"Abstract Geometric semantic genetic programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"20 4","pages":"0"},"PeriodicalIF":1.7000,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genetic Programming and Evolvable Machines","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s10710-023-09463-1","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Geometric semantic genetic programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.
期刊介绍:
A unique source reporting on methods for artificial evolution of programs and machines...
Reports innovative and significant progress in automatic evolution of software and hardware.
Features both theoretical and application papers.
Covers hardware implementations, artificial life, molecular computing and emergent computation techniques.
Examines such related topics as evolutionary algorithms with variable-size genomes, alternate methods of program induction, approaches to engineering systems development based on embryology, morphogenesis or other techniques inspired by adaptive natural systems.