Graph representations in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Sotto:GPEM,

• author = "Leo Francoso Dal Piccol Sotto and Paul Kaufmann and Timothy Atkinson and Roman Kalkreuth and Marcio Porto Basgalupp",
• title = "Graph representations in genetic programming",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2021",
• volume = "22",
• number = "4",
• pages = "607--636",
• month = dec,
• note = "Special Issue: Highlights of Genetic Programming 2020 Events",
• keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Linear genetic programming, Evolving graphs by graph programming, Directed acyclic graph",
• ISSN = "1389-2576",
• URL = "https://rdcu.be/cyKGD",
• DOI = "doi:10.1007/s10710-021-09413-9",
• size = "30 pages",
• abstract = "Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and (1+lambda). In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the (1+lambda) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP number of registers and CGP levels back parameter is of utmost importance and contributes significantly to better convergence of an optimisation algorithm when solving complex problems that benefit from code reuse.",
• notes = "Fraunhofer SCAI, Sankt Augustin, Germany",

}

Genetic Programming entries for Leo Francoso Dal Piccol Sotto Paul Kaufmann Timothy Atkinson Roman Tobias Kalkreuth Marcio Porto Basgalupp

Citations