Research ArticleOpen AccessGoogle Scholar indexed
On Some Basic Concepts of Genetic Algorithms as a Meta-Heuristic Method for Solving of Optimization Problems
- 1
Journal of Software Engineering and Applications·Volume 04 (2011)·Pages 482–486·Published 29 August 2011·DOI10.4236/jsea.2011.48055
Copy link · social · email
Abstract
The genetic algorithms represent a family of algorithms using some of genetic principles being present in nature, in order to solve particular computational problems. These natural principles are: inheritance, crossover, mutation, survival of the fittest, migrations and so on. The paper describes the most important aspects of a genetic algorithm as a stochastic method for solving various classes of optimization problems. It also describes the basic genetic operator selection, crossover and mutation, serving for a new generation of individuals to achieve an optimal or a good enough solution of an optimization problem being in question.
KeywordsGenetic AlgorithmIndividualsGenetic OperatorSelectionCrossoverMutation
- V. Filipovic, “A Proposal to Improve Operator Tournament Selection in Genetic Algorithms,” Msa Thesis, Faculty of Mathematics, University of Belgrade, 1998, (in Serbian).
- V. Filipovic, J. Kratica, D. Tosic and I. Ljubic, “Fine Grained Tournament Selection for the Simple Plant Location Problem,” Proceedings on the 5th Online World Conference on Soft Computing Methods in Industrial Application—WSC5, 2000, pp. 152-158.
- V. Filipovic, D. Tosic and J Kratica, “Experimental Results in Applying of Fine Grained Tournament Selection,” Proceedings of the 10th Congress of Yugoslav Mathematicians, Belgrade, 21-24 January 2001, pp. 331-336.
- V. Filipovic, “Fine-Grained Tournament Selection Operator in Genetic Algorithms,” Computing and Informatics, Vol. 22, No. 2, 2003, pp. 143-161.
- F. J. Burkowski, “Shuffle Crossover and Mutual Information,” Proceedings of the 1999 Congres on Evolutionary Computation, 1999, pp. 1574-1580.
- L. Booker, “Improving Search in Genetic Algorithms: In Algorithms and Simulated Annealing,” Morgan Kaufmann Publichers, San Mateo, 1987, pp. 61-73.
- H. Muhlenbein and D. Schlierkamp-Voosen, “Predictive Models for the Breeder Genetic Algorithm: I. Continuous Parameter Optimization,” Evolutionary Computation, Vol. 1, No. 1, 1993, pp. 25-49. doi:10.1162/evco.1993.1.1.25
- C. Hohn and C. R. Reeves, “Embedding Neighbour-Hoodsearch Operators in a Genetic Algorithm for Graph Bipartitioning,” Ctac Technical Report, Coventry University, Reeves, 1995, pp. 33-34.
- J. Kratica, “Parallelization of Genetic Algorithms for Solving Some NP-complete Problems,” Ph.D. Thesis, Faculty of Mathematics, Belgrade, 2000, (in Serbian).
- D. Tosic, N. Mladenovic, J. Kratica and V. Filpovic, “Genetic Algorithm,” Institute of Mathematics SANU, Beograd, 2004, (in Serbian).
- J. Kratica, “Improvement of Simple Genetic Algorithm for Solving the Uncapacitated Warehouse Location Problem,” In: R. Roy, T. Furuhachi and P. K. Chawdhry, Eds., Advances in Soft Computing—Engineering Design and Manufactining, Springer-Verlang London Limited, London, 1999, pp. 390-402.