Imperative Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8592

@Article{fajfar:2024:Symmetry,

• author = "Iztok Fajfar and Ziga Rojec and Arpad Burmen and Matevz Kunaver and Tadej Tuma and Saso Tomazic and Janez Puhan",
• title = "Imperative Genetic Programming",
• journal = "Symmetry",
• year = "2024",
• volume = "16",
• number = "9",
• pages = "Article No. 1146",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2073-8994",
• URL = "https://www.mdpi.com/2073-8994/16/9/1146",
• DOI = "doi:10.3390/sym16091146",
• abstract = "Genetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly using tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve 'real' Turing-like imperative programs (as contrasted with functional programming) from the ground up. Existing research focuses mainly on specific special cases where the structure of the solution is partly known. This paper explores the potential of integrating tree and linear GP paradigms to develop an encoding scheme that universally supports genetic operators without constraints and consistently generates syntactically correct Python programs from scratch. By blending the symmetrical structure of tree-based representations with the inherent asymmetry of linear sequences, we created a versatile environment for program evolution. Our approach was rigorously tested on 35 problems characterised by varying Halstead complexity metrics, to delineate the approach's boundaries. While expected brute-force program solutions were observed, our method yielded more sophisticated strategies, such as optimising a program by restricting the division trials to the values up to the square root of the number when counting its proper divisors. Despite the recent groundbreaking advancements in large language models, we assert that the GP field warrants continued research. GP embodies a fundamentally different computational paradigm, crucial for advancing our understanding of natural evolutionary processes.",
• notes = "also known as \cite{sym16091146}",

}

Genetic Programming entries for Iztok Fajfar Ziga Rojec Arpad Burmen Matevz Kunaver Tadej Tuma Saso Tomazic Janez Puhan

Citations