Genetic algorithms are a powerful tool for solving search and optimization problems. We examine the problems associated with applying a genetic algorithm and develop a new computer program to facilitate the rapid application of genetic algorithms. This system is intended to function as a test bed for GA research or as a general purpose solution mechanism. Our approach is to place all of the common non-problem specific logic in a menu driven testbed program. The problem specific objective function is created separately by the user and is treated as an external program. The testbed program is capable of interfacing with the external objective function whenever necessary. Maximum flexibility in applying genetic algorithms is obtained by making...
Abstract — Genetic_Algorithms (GAs) have been considered to automate the generation of test-data for...
This paper provides an introduction to genetic algorithms and genetic programming and lists sources ...
This book sets out to explain what genetic algorithms are and how they can be used to solve real-wor...
Genetic algorithms are a powerful tool for solving search and optimization problems. We examine the ...
Genetic programming (GP) is an automated method for creating a working computer program from a high-...
In this study, the solution of the problem of generating an intelligent test paper with a genetic al...
Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to le...
Although it is well understood to be a generally undecidable problem, a number of attempts have been...
Introduction Genetic programming is a domain-independent problem-solving approach in which computer ...
Abstract: Genetic algorithms are search and optimization techniques which have their origin and insp...
Genetic algorithms (GA'S) are global, parallel, stochastic search methods, founded on Darwinian evol...
This paper presents a prototype of the toolkit which makes it easy to conduct experiments of genetic...
Use of a genetic algorithm and formal concept analysis to generate test data for branch coverage is ...
Evolutionary Algorithms started in the 1950's with [Fra57] and [Box57]. They form a powerful fa...
Genetic Algorithms (GAs) are one of several techniques in the family of Evolutionary Algorithms - al...
Abstract — Genetic_Algorithms (GAs) have been considered to automate the generation of test-data for...
This paper provides an introduction to genetic algorithms and genetic programming and lists sources ...
This book sets out to explain what genetic algorithms are and how they can be used to solve real-wor...
Genetic algorithms are a powerful tool for solving search and optimization problems. We examine the ...
Genetic programming (GP) is an automated method for creating a working computer program from a high-...
In this study, the solution of the problem of generating an intelligent test paper with a genetic al...
Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to le...
Although it is well understood to be a generally undecidable problem, a number of attempts have been...
Introduction Genetic programming is a domain-independent problem-solving approach in which computer ...
Abstract: Genetic algorithms are search and optimization techniques which have their origin and insp...
Genetic algorithms (GA'S) are global, parallel, stochastic search methods, founded on Darwinian evol...
This paper presents a prototype of the toolkit which makes it easy to conduct experiments of genetic...
Use of a genetic algorithm and formal concept analysis to generate test data for branch coverage is ...
Evolutionary Algorithms started in the 1950's with [Fra57] and [Box57]. They form a powerful fa...
Genetic Algorithms (GAs) are one of several techniques in the family of Evolutionary Algorithms - al...
Abstract — Genetic_Algorithms (GAs) have been considered to automate the generation of test-data for...
This paper provides an introduction to genetic algorithms and genetic programming and lists sources ...
This book sets out to explain what genetic algorithms are and how they can be used to solve real-wor...