Improving the Efficiency Of Genetic Programming for Classification Tasks Using a Phased Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{chitty:2024:GECCOcomp2,

• author = "Darren Chitty",
• title = "Improving the Efficiency Of Genetic Programming for Classification Tasks Using a Phased Approach",
• booktitle = "9th Workshop on Industrial Applications of Metaheuristics (IAM 2024)",
• year = "2024",
• editor = "Silvino Fernandez Alzueta and Thomas Stuetzle",
• pages = "1702--1705",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, high performance computing, efficiency",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3664184",
• size = "4 pages",
• abstract = "Genetic Programming (GP) uses Darwinian evolution to generate algorithms for tasks such as classification and symbolic regression. However, a drawback is the interpreter used to evaluate candidate programs adding significant computational cost. Hence, many studies have sought to improve the speed of GP primarily via parallelism. However, efficiency can also offer considerable performance gains. GP has recently been applied to combinatorial optimisation using a phased approach (Phased-GP) whereby programs are evolved piecemeal avoiding reinterpretation of sub-programs. This method was found to be highly effective and efficient compared to standard GP. This paper investigates if a similar effect is observed when using phased GP to incrementally build classifiers. Tested upon known real-world classification problems, an efficiency saving of up to 98\% can be achieved with a speedup of 70 fold and no significant loss of classification accuracy. Moreover, the method can be easily used within any existing high performance parallel GP models.",
• notes = "GECCO-2024 IAM A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Darren M Chitty

Citations