EVOLUTIONARY ALORITHMS AT CHOICE: FROM GA TO GP
DOI:
https://doi.org/10.17770/etr2009vol2.1023Keywords:
evolutionary algorithms, genetic algorithm, genetic programmingAbstract
Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. Evolutionary algorithms are stochastic search methods that try to emulate Darwin’s principle of natural evolution. There are (at least) four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyzes present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. The paper presents implementation examples that show the working principles of evolutionary algorithms.Downloads
References
Weise T. Global Optimization Algorithms - Theory and Application, 2008. URL: http://www.itweise. de/index.html - Visit date January 2009.
Mitchell M. An introduction to Genetic Algorithms. A Bradford Book The MIT Press, 1999.
Introduction on Evolutionary Algorithms. URL: http://neo.lcc.uma.es/opticomm/introea.html - Visit date January 2009.
Holland J.H. Adaptation in Natural and Artificial Systems. Univ. of Michigan Press: Ann Arbor. Reprinted in 1992 by MIT Press, Cambridge MA.
A Field Guide to Genetic Programming. URL: http://www.gp-field-guide.org.uk/ - Visit date January 2009.
Haupt R.L., Haupt S.E. Practical Genetic Algorithms. John Wiley & Sons, 2004.
Karr C., Freeman L.M. Industrial Applications of Genetic Algorithms. International Series on Computational Intelligence: CRC Press, 1999.
Koza J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge: MIT Press, 1992.
Banzhaf W., Nordin P., Keller R.E. and Francone F.D. Genetic Programming- An Introduction. Morgan Kaufmann Publishers, 1998.