Authors: Andrew Owusu-Hemeng1, Peter Kwasi Sarpong2, Joseph Ackora-Prah3
1,2,3Department of Mathematics, Kwame Nkrumah University of Science and Technology,Kumasi,Ghana
Email: owusuhemengandrew@gmail.com, kp.sarp@yahoo.co.uk, ackph@yahoo.co.uk
Abstract
Genetic Algorithms are a family of computational models inspired by evolution or Genetic Algorithms are search algorithms that are based on concepts of natural selection and natural genetics. Thus GA is a stochastic global search method that mimics the metaphor of natural biological evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome-like data structure and apply recombination operators to these structures so as to preserve critical information. Genetic algorithm was developed to simulate some of the processes observed in natural evolution, a process that operates on chromosomes (organic devices for encoding the structure of living being). GAs operate on a number of potential solutions, called a population, consisting of some encoding of the parameter set simultaneously and applying the principle of survival of the fittest to produce (hopefully) better and better approximations to a solution. Genetic algorithms are often viewed as function optimizer, although the range of problems to which genetic algorithms have been applied are quite broad. An implementation of genetic algorithm begins with a population of (typically random) chromosomes. A new population is created by allowing parent solutions in one generation to produce offspring, which are included in the next generation. A ‘survival of the fittest’ principle is applied to ensure that the overall quality of solutions increases as the algorithm progresses from one generation to the next. The overall structure of genetic algorithm is as follows Gen and Cheng (200), Goldberg (1989)): Selection & Crossover
Keywords: Genetic Algorithm, Computational Models
