Genetic Programming

2011 ◽  
pp. 529-533
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Programming ◽  
Objective Function ◽  
Evolutionary Computation ◽  
Computer Programs ◽  
Fitness Criterion

Genetic programming (GP) is a subarea of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function.

Genetic Programming

2011 ◽  
pp. 293-307
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Programming ◽  
Objective Function ◽  
Evolutionary Computation ◽  
Computer Programs ◽  
Fitness Criterion

Genetic programming (GP) is a subarea of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function.

scholarly journals GAPS: A Genetic Programming System

2003 ◽  
Vol 12 (02) ◽  
pp. 187-206 ◽  
Author(s):  
Du Zhang ◽  
Michael D. Kramer
Keyword(s):  
Genetic Programming ◽  
Computer Program ◽  
Evolutionary Computation ◽  
Computer Programs ◽  
Initial Problem ◽  
Evolution Process ◽  
Programming System ◽  
Problem Statement ◽  
Termination Criteria

One of the major approaches in the field of evolutionary computation is genetic programming. Genetic programming tackles the issue of how to automatically create a computer program for a given problem from some initial problem statement. The goal is accomplished by genetically breeding a population of computer programs in terms of genetic operations. In this paper, we describe a genetic programming system called GAPS. GAPS has the following features: (1) It implements the standard generational algorithm for genetic programming with some refinement on controlling introns growth during evolution process and improved termination criteria. (2) It includes an extensible language tailored to the needs of genetic programming. And (3) It is a complete, standalone system that allows for genetic programming tasks to be carried out without requiring other tools such as compilers. Results with GAPS have been satisfactory.

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.

scholarly journals Evolutionary Algorithms for Boolean Functions in Diverse Domains of Cryptography

2016 ◽  
Vol 24 (4) ◽  
pp. 667-694 ◽  
Author(s):  
Stjepan Picek ◽  
Claude Carlet ◽  
Sylvain Guilley ◽  
Julian F. Miller ◽  
Domagoj Jakobovic
Keyword(s):  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Boolean Functions ◽  
Coding Theory ◽  
Design Criteria ◽  
Hamming Weight ◽  
High Quality ◽  
Distinct Role

The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.

scholarly journals On the Evolvability of a Hybrid Ant Colony-Cartesian Genetic Programming Methodology

2021 ◽  
Author(s):  
Sweeney Luis
Keyword(s):  
Genetic Programming ◽  
Dynamic Environment ◽  
Computer Programs ◽  
Hybrid Architecture ◽  
Ant System ◽  
Cartesian Genetic Programming ◽  
Ant Colonies ◽  
Programming Methodology ◽  
Genotype Diversity ◽  
Evolution Proceeds

In this thesis, we design a method that uses Ant Colonies as a Model-based Search to Cartesian Genetic Programming (CGP) to induce computer programs. Candidate problem solutions are encoded using a CGP representation. Ants generate problem solutions guided by pheromone traces of entities and nodes of the CGP representation. The pheromone values are updated based on the paths followed by the best ants, as suggested in the Rank-Based Ant System (ASrank). To assess the evolvability of the system we applied a modified version of the method introduced in [1] to measure rate of evolution which considers variability and neutrality as the major influences in the evolution of a system. Our results show that such method effectively reveals how evolution proceeds under different parameter settings and different environmental scenarios. The proposed hybrid architecture shows high evolvability in a dynamic environment by maintaining a pheromone model that elicits high genotype diversity.

scholarly journals On the Evolvability of a Hybrid Ant Colony-Cartesian Genetic Programming Methodology

2021 ◽  
Author(s):  
Sweeney Luis
Keyword(s):  
Genetic Programming ◽  
Dynamic Environment ◽  
Computer Programs ◽  
Hybrid Architecture ◽  
Ant System ◽  
Cartesian Genetic Programming ◽  
Ant Colonies ◽  
Programming Methodology ◽  
Genotype Diversity ◽  
Evolution Proceeds

In this thesis, we design a method that uses Ant Colonies as a Model-based Search to Cartesian Genetic Programming (CGP) to induce computer programs. Candidate problem solutions are encoded using a CGP representation. Ants generate problem solutions guided by pheromone traces of entities and nodes of the CGP representation. The pheromone values are updated based on the paths followed by the best ants, as suggested in the Rank-Based Ant System (ASrank). To assess the evolvability of the system we applied a modified version of the method introduced in [1] to measure rate of evolution which considers variability and neutrality as the major influences in the evolution of a system. Our results show that such method effectively reveals how evolution proceeds under different parameter settings and different environmental scenarios. The proposed hybrid architecture shows high evolvability in a dynamic environment by maintaining a pheromone model that elicits high genotype diversity.

Efficient Evolution of Modular Robot Control via Genetic Programming

2013 ◽  
pp. 59-85
Author(s):  
Tüze Kuyucu ◽  
Ivan Tanev ◽  
Katsunori Shimohara
Keyword(s):  
Multiobjective Optimization ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Fast Moving ◽  
Search Space ◽  
Multiple Objectives ◽  
Experimental Results ◽  
Robotic Systems ◽  
Linear Fashion ◽  
Fast Evolution

In Genetic Programming (GP), most often the search space grows in a greater than linear fashion as the number of tasks required to be accomplished increases. This is a cause for one of the greatest problems in Evolutionary Computation (EC): scalability. The aim of the work presented here is to facilitate the evolution of control systems for complex robotic systems. The authors use a combination of mechanisms specifically designed to facilitate the fast evolution of systems with multiple objectives. These mechanisms are: a genetic transposition inspired seeding, a strongly-typed crossover, and a multiobjective optimization. The authors demonstrate that, when used together, these mechanisms not only improve the performance of GP but also the reliability of the final designs. They investigate the effect of the aforementioned mechanisms on the efficiency of GP employed for the coevolution of locomotion gaits and sensing of a simulated snake-like robot (Snakebot). Experimental results show that the mechanisms set forth contribute to significant increase in the efficiency of the evolution of fast moving and sensing Snakebots as well as the robustness of the final designs.

Soft Computing Techniques in Civil Engineering

2010 ◽  
pp. 143-159
Author(s):  
Juan L. Pérez ◽  
Juan R. Rabuñal ◽  
Fernando Martínez Abella
Keyword(s):  
Genetic Programming ◽  
Evolutionary Computation ◽  
Soft Computing ◽  
Civil Engineering ◽  
Structural Concrete ◽  
Controlled Conditions ◽  
Classical Algorithm ◽  
Soft Computing Techniques ◽  
Over Time

Soft computing techniques are applied to a huge quantity of problems spread in several areas of science. In this case, Evolutionary Computation (EC) techniques are applied, in concrete Genetic Programming (GP), to a temporary problem associated to the field of Civil Engineering. The case of study of this technique has been centered in the prediction, over time, of the behavior of the structural concrete in controlled conditions. Given the temporary nature of the case of study, it has been necessary to make several changes to the classical algorithm of GP, among whom it can be emphasized the incorporation of a new operator that gives the GP the ability to be able to solve problems with temporary behavior. The obtained results shown that the proposed method has succeeded in improving the adjustment to the current regulations about creep in the structural concrete.

Soft Computing Techniques in Civil Engineering

2012 ◽  
pp. 1982-1997
Author(s):  
Juan L. Pérez ◽  
Juan Rabuñal ◽  
Fernando Martínez Abella
Keyword(s):  
Genetic Programming ◽  
Evolutionary Computation ◽  
Soft Computing ◽  
Civil Engineering ◽  
Structural Concrete ◽  
Controlled Conditions ◽  
Classical Algorithm ◽  
Soft Computing Techniques ◽  
Over Time

Soft computing techniques are applied to a huge quantity of problems spread in several areas of science. In this case, Evolutionary Computation (EC) techniques are applied, in concrete Genetic Programming (GP), to a temporary problem associated to the field of Civil Engineering. The case of study of this technique has been centered in the prediction, over time, of the behavior of the structural concrete in controlled conditions. Given the temporary nature of the case of study, it has been necessary to make several changes to the classical algorithm of GP, among whom it can be emphasized the incorporation of a new operator that gives the GP the ability to be able to solve problems with temporary behavior. The obtained results shown that the proposed method has succeeded in improving the adjustment to the current regulations about creep in the structural concrete.

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