Towards Understanding and Refining the General Program Synthesis Benchmark Suite with Genetic Programming

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

@InProceedings{Forstenlechner:2018:CEC,

• author = "Stefan Forstenlechner and David Fagan and Miguel Nicolau and Michael O'Neill",
• title = "Towards Understanding and Refining the General Program Synthesis Benchmark Suite with Genetic Programming",
• booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2018",
• editor = "Marley Vellasco",
• address = "Rio de Janeiro, Brazil",
• month = "8-13 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CEC.2018.8477953",
• abstract = "Program synthesis is a complex problem domain tackled by many communities via different methods. In the last few years, a lot of progress has been made with Genetic Programming (GP) on solving a variety of general program synthesis problems for which a benchmark suite has been introduced. While Genetic Programming is capable of finding correct solutions for many problems contained in a general program synthesis problems benchmark suite, the actual success rate per problem is low in most cases. In this paper, we analyse certain aspects of the benchmark suite and the computational effort required to solve its problems. A subset of problems on which GP performs poorly is identified. This subset is analysed to find measures to increase success rates for similar problems. The paper concludes with suggestions to refine performance on program synthesis problems.",
• notes = "WCCI2018",

}

Genetic Programming entries for Stefan Forstenlechner David Fagan Miguel Nicolau Michael O'Neill

Citations