Accelerating convergence in cartesian genetic programming by using a new genetic operator

Meier, Andreas; Gonter, Mark; Kruse, Rudolf

doi:10.1145/2463372.2463481

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…Meier et al interpreted the RVCGP genes as mean values in a multivariate Gaussian distribution [63]. From these distributions new genotypes can be sampled.…”

Section: Real-valuedmentioning

confidence: 99%

Cartesian genetic programming: its status and future

Miller

2019

Genet Program Evolvable Mach

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Cartesian genetic programming, a well-established method of genetic programming, is approximately 20 years old. It represents solutions to computational problems as graphs. Its genetic encoding includes explicitly redundant genes which are wellknown to assist in effective evolutionary search. In this article, we review and compare many of the important aspects of the method and findings discussed since its inception. In the process, we make many suggestions for further work which could improve the efficiency of the CGP for solving computational problems.

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“…Meier et al interpreted the RVCGP genes as mean values in a multivariate Gaussian distribution [63]. From these distributions new genotypes can be sampled.…”

Section: Real-valuedmentioning

confidence: 99%

Cartesian genetic programming: its status and future

Miller

2019

Genet Program Evolvable Mach

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“…Differently from other algorithms, GP, as stated in [11,12], has in its favor the production of symbolic models, allowing knowledge extraction from the model by an area expert. Although GP is able to generate interpretable models [13,14], it demands high computational cost, given the number of objective function evaluations required to reach a good solution. When modeling dynamical phenomena, for example, it may require the numerical solution of ordinary differential equations during the evaluation phase.…”

Section: Grammar-based Genetic Programmingmentioning

confidence: 99%

A Hybrid Approach of Grammar-based Genetic Programming and Differential Evolution for Symbolic Regression

Motta¹,

Freitas²,

Souza³

et al. 2018

Proceedings XIII Brazilian Congress on Computational Inteligence

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Genetic Programming (GP) is used for solving many real world problems. From data classification to building phylogenetic trees, the technique can be applied to almost any problem. One way to improve GP performance is by using a formal grammar. We propose here the use of grammar-based genetic programming (GGP) with Differential Evolution (DE). DE is incorporated to GGP in order to improve the quality of solutions obtained by GGP by finding numerical coefficients when solving symbolic regression problems. In this proposal, the coefficients of the best individuals generated by GGP during the search are adjusted by DE. Also, this technique incorporates these values to the grammar; thus, the grammar is adapted during the search. The proposed method is applied to 24 symbolic regression problems and it is compared to a standard GGP. The results indicate that GGP hybridized with DE obtained better models, specially when one expects real-valued coefficients in the model.

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“…Despite all these GP successes and applications, standard breeding operators can spoil promising solutions, and there are some risks that the optimal structure will be difficult to find. Therefore, there have been many attempts to modify GP operators with the purpose of maintaining promising individuals and reaching optimal solutions [13,22].…”

Section: Introductionmentioning

confidence: 99%

Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search

2022

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The foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many studies have been conducted on GP, there are many questions about the disruption effect of the main GP breeding operators, i.e., crossover and mutation. Moreover, this method often suffers from high computational costs when implemented in some complex applications. This paper presents the meta-heuristics programming framework to create new practical machine learning tools alternative to the GP method. Furthermore, the immune system programming with local search (ISPLS) algorithm is composed from the proposed framework to enhance the classical artificial immune system algorithm with the tree data structure to deal with machine learning applications. The ISPLS method uses a set of breeding procedures over a tree space with gradual changes in order to surmount the defects of GP, especially the high disruptions of its basic operations. The efficiency of the proposed ISPLS method was proven through several numerical experiments, including promising results for symbolic regression, 6-bit multiplexer and 3-bit even-parity problems.

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Accelerating convergence in cartesian genetic programming by using a new genetic operator

Cited by 11 publications

References 10 publications

Cartesian genetic programming: its status and future

Cartesian genetic programming: its status and future

A Hybrid Approach of Grammar-based Genetic Programming and Differential Evolution for Symbolic Regression

Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search

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