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.

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.

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.

Cooperation Between Agents to Evolve Complete Programs

2011 ◽  
pp. 213-228 ◽  
Author(s):  
Ricardo Aler ◽  
David Camacho ◽  
Alfredo Moscardini
Keyword(s):  
Genetic Programming ◽  
Computer Program ◽  
System Approach ◽  
Multiagent System ◽  
Computer Programs ◽  
Main Body ◽  
Programming Paradigm

In this paper, we present a multiagent system approach with the purpose of building computer programs. Each agent in the multiagent system will be in charge of evolving a part of the program, which in this case, can be the main body of the program or one of its different subroutines. There are two kinds of agents: the manager agent and the genetic programming (GP) agents. The former is in charge of starting the system and returning the results to the user. The GP agents include skills for evolving computer programs, based on the genetic programming paradigm. There are two sorts of GP agents: some can evolve the main body of the program and the others evolve its subroutines. Both kinds of agents cooperate by telling each other their best results found so far, so that the search for a good computer program is made more efficient. In this paper, this multiagent approach is presented and tested empirically.

Comparative assessment of informational content of the equations of F.P. Hadlock and the computer program of V.N. Demidov in determination of term of gestation and fetal weight in the III trimester of gestation

2017 ◽  
Author(s):  
E.A. Derkach , O.I. Guseva
Keyword(s):  
Computer Program ◽  
High Precision ◽  
Gestational Age ◽  
Computer Programs ◽  
Comparative Assessment ◽  
Fetal Weight ◽  
Informational Content ◽  
Third Trimester ◽  
The Third

Objectives: to compare the accuracy of equations F.P. Hadlock and computer programs by V.N. Demidov in determining gestational age and fetal weight in the third trimester of gestation. Materials: 328 patients in terms 36–42 weeks of gestation are examined. Ultrasonography was performed in 0–5 days prior to childbirth. Results: it is established that the average mistake in determination of term of pregnancy when using the equation of F.P. Hadlock made 12,5 days, the computer program of V.N. Demidov – 4,4 days (distinction 2,8 times). The mistake within 4 days, when using the equation of F.P. Hadlock has met on average in 23,1 % of observations, the computer program of V.N. Demidov — 65,9 % (difference in 2,9 times). The mistake more than 10 days, took place respectively in 51,7 and 8,2 % (distinction by 6,3 times). At a comparative assessment of size of a mistake in determination of fetal mass it is established that when using the equation of F.P. Hadlock it has averaged 281,0 g, at application of the computer program of V.N. Demidov — 182,5 g (distinction of 54 %). The small mistake in the mass of a fetus which isn't exceeding 200 g at application of the equation of F.P. Hadlock has met in 48,1 % of cases and the computer program of V.N. Demidov — 64,0 % (distinction of 33,1 %). The mistake exceeding 500 g has been stated in 18 % (F.P. Hadlock) and 4,3 % (V.N. Demidov) respectively (distinction 4,2 times). Conclusions: the computer program of V.N. Demidov has high precision in determination of term of a gestation and mass of a fetus in the III pregnancy.

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 Computer programs for calculating pKa: A comparative study for 3-[3-(2-nitrophenyl)prop-2-enoyl]-2h-1-benzopyran-2-one

2010 ◽  
Vol 75 (2) ◽  
pp. 243-248 ◽  
Author(s):  
Selma Spirtovic-Halilovic ◽  
Davorka Zavrsnik
Keyword(s):  
Antimicrobial Activity ◽  
Computer Program ◽  
Dissociation Constant ◽  
Chemical Compounds ◽  
Computer Programs ◽  
Good Activity ◽  
Target Compound ◽  
Theoretical Approaches ◽  
Pka Value ◽  
Good Agreement

Coumarin-based compounds containing a chalcone moiety exhibit antimicrobial activity. These substances are potential drugs and it is important to determine their pKa values. However, they are almost insoluble in water. The dissociation constant was experimentally determined by potentiometric titration for 3-[3-(2-nitrophenyl)prop-2-enoyl]-2H-1-benzopyran-2-one because this compound shows good activity and solubility. A number of different computer programs for the calculation of the dissociation constant of chemical compounds have been developed. The pKa value of the target compound was calculated using three different computer programs, i.e., the ACD/pKa, CSpKaPredictor and ADME/ToxWEB programs, which are based on different theoretical approaches. The analysis demonstrated good agreement between the experimentally observed pKa value of 3-[3-(2-nitrophenyl)prop-2-enoyl]-2H-1-benzopyran- 2-one and the value calculated using the computer program CSpKa.

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