CRust_GP: A Framework for the Modular Construction and Evaluation of Cartesian Genetic Programming in Rust

Research in Cartesian Genetic Programming (CGP) is—in most works—of empirical nature. To further facilitate this, we present CRust_GP: A Rust framework for the construction of (generic) CGP varieties and their configuration. Furthermore, it includes various evolutionary operators and extensions that are commonly found in the CGP related literature. By using a component based architecture, we achieve high modularity and simplify the process of implementing extensions. This paper provides a general description of CRust_GP's features, integrated evolutionary operators, as well as its high level architectural design.

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Publication Details

Journal
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Published
2025-07-14
DOI
https://doi.org/10.1145/3712255.3726534
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

CRust_GP: A Framework for the Modular Construction and Evaluation of Cartesian Genetic Programming in Rust

Jörg Hähner, Henning Cui
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Evolutionary Algorithms and Applications
article

CRust_GP: A Framework for the Modular Construction and Evaluation of Cartesian Genetic Programming in Rust

Jörg Hähner, Henning Cui
article en

Abstract

Research in Cartesian Genetic Programming (CGP) is—in most works—of empirical nature. To further facilitate this, we present CRust_GP: A Rust framework for the construction of (generic) CGP varieties and their configuration. Furthermore, it includes various evolutionary operators and extensions that are commonly found in the CGP related literature. By using a component based architecture, we achieve high modularity and simplify the process of implementing extensions. This paper provides a general description of CRust_GP's features, integrated evolutionary operators, as well as its high level architectural design.

Proceedings of the Genetic and Evolutionary Computation Conference Companion
University of Augsburg (DE)
Openalex Percentile: Top 62%
Evolutionary Algorithms and Applications
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