scholarly journals FMCGP: frameshift mutation cartesian genetic programming

2021 ◽  
Author(s):  
Wei Fang ◽  
Mindan Gu
Keyword(s):  
Genetic Programming ◽  
Frameshift Mutation ◽  
Population Diversity ◽  
Search Performance ◽  
Mutation Operator ◽  
Cartesian Genetic Programming ◽  
Dna Mutation ◽  
Regression Problems ◽  
The Individual ◽  
Acyclic Directed Graph

AbstractCartesian Genetic Programming (CGP) is a variant of Genetic Programming (GP) with the individuals represented by a two-dimensional acyclic directed graph, which can flexibly encode many computing structures. In general, CGP only uses a point mutation operator and the genotype of an individual is of fixed size, which may lead to the lack of population diversity and then cause the premature convergence. To address this problem in CGP, we propose a Frameshift Mutation Cartesian Genetic Programming (FMCGP), which is inspired by the DNA mutation mechanism in biology and the frameshift mutation caused by insertion or deletion of nodes is introduced to CGP. The individual in FMCGP has variable-length genotype and the proposed frameshift mutation operator helps to generate more diverse offspring individuals by changing the compiling framework of genotype. FMCGP is evaluated on the symbolic regression problems and Even-parity problems. Experimental results show that FMCGP does not exhibit the bloat problem and the use of frameshift mutation improves the search performance of the standard CGP.

scholarly journals Graph representations in genetic programming

2021 ◽  
Author(s):  
Léo Françoso Dal Piccol Sotto ◽  
Paul Kaufmann ◽  
Timothy Atkinson ◽  
Roman Kalkreuth ◽  
Márcio Porto Basgalupp
Keyword(s):  
Genetic Programming ◽  
Evolutionary Algorithm ◽  
Digital Circuit ◽  
Search Performance ◽  
Genetic Operators ◽  
Code Reuse ◽  
Neutral Drift ◽  
Graph Representations ◽  
Regression Problems ◽  
The Individual

AbstractGraph 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. In this work, 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 $$(1+\lambda )$$ ( 1 + λ ) . 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 ($$1 + \lambda$$ 1 + λ ) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP’s number of registers and CGP’s levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse.

Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming

2019 ◽  
Vol 27 (3) ◽  
pp. 497-523 ◽  
Author(s):  
Michaela Drahosova ◽  
Lukas Sekanina ◽  
Michal Wiglasz
Keyword(s):  
Genetic Programming ◽  
Evolutionary Algorithm ◽  
Filter Design ◽  
Training Data ◽  
Cartesian Genetic Programming ◽  
Design Problems ◽  
Quality Of Results ◽  
Regression Problems ◽  
Time Required

In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.

An Analysis of the Influence of Non-effective Instructions in Linear Genetic Programming

2021 ◽  
pp. 1-23
Author(s):  
Léo Françoso Dal Piccol Sotto ◽  
Franz Rothlauf ◽  
Vinçcius Veloso de Melo ◽  
Márcio P. Basgalupp
Keyword(s):  
Genetic Programming ◽  
Directed Acyclic Graph ◽  
Genetic Material ◽  
Population Diversity ◽  
Search Performance ◽  
Linear Genetic Programming ◽  
Improve Performance ◽  
Neutral Mutations ◽  
Evolutionary Memory

Abstract Linear Genetic Programming (LGP) represents programs as sequences of instructions and has a Directed Acyclic Graph (DAG) dataflow. The results of instructions are stored in registers that can be used as arguments by other instructions. Instructions that are disconnected from the main part of the program are called non-effective instructions, or structural introns. They also appear in other DAG-based GP approaches like Cartesian Genetic Programming (CGP). This paper studies four hypotheses on the role of structural introns: non-effective instructions (1) serve as evolutionary memory, where evolved information is stored and later used in search, (2) preserve population diversity, (3) allow neutral search, where structural introns increase the number of neutral mutations and improve performance, and (4) serve as genetic material to enable program growth. We study different variants of LGP controlling the influence of introns for symbolic regression, classification, and digital circuits problems. We find that there is (1) evolved information in the non-effective instructions that can be reactivated and that (2) structural introns can promote programs with higher effective diversity. However, both effects have no influence on LGP search performance. On the other hand, allowing mutations to not only be applied to effective but also to noneffective instructions (3) increases the rate of neutral mutations and (4) contributes to program growth by making use of the genetic material available as structural introns. This comes along with a significant increase of LGP performance, which makes structural introns important for LGP.

scholarly journals A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS

2009 ◽  
Vol 18 (05) ◽  
pp. 757-781 ◽  
Author(s):  
CÉSAR L. ALONSO ◽  
JOSÉ LUIS MONTAÑA ◽  
JORGE PUENTE ◽  
CRUZ ENRIQUE BORGES
Keyword(s):  
Data Structure ◽  
Genetic Programming ◽  
Computer Programs ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Linear Genetic Programming ◽  
Straight Line ◽  
Structured Representations ◽  
Regression Problems ◽  
Straight Line Programs

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.

Discriminating normal and cancerous thyroid cell lines using implicit context representation Cartesian genetic programming

2010 ◽  
Author(s):  
Michael A. Lones ◽  
Stephen L. Smith ◽  
Andrew T. Harris ◽  
Alec S. High ◽  
Sheila E. Fisher ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Cell Lines ◽  
Thyroid Cell ◽  
Cartesian Genetic Programming
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