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Reseach Article
Variable Mutation Rate at Genetic Algorithms: Introduction of Chromosome Fitness in Connection with Multi-Chromosome Representation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 72 - Number 17 |
| Year of Publication: 2013 |
| Authors: Matthias Kuhn, Thomas Severin, Horst Salzwedel |
10.5120/12636-9343
|
Matthias Kuhn, Thomas Severin, Horst Salzwedel . Variable Mutation Rate at Genetic Algorithms: Introduction of Chromosome Fitness in Connection with Multi-Chromosome Representation. International Journal of Computer Applications. 72, 17 ( June 2013), 31-38. DOI=10.5120/12636-9343
Abstract
For genetic algorithms (GAs) researchers look for optimal control parameters, such as population size or mutation rate. Early research was carried out using constant control parameters to find optimal parameter values for GA. The findings are only specific to the considered problem and therefore not suitable to be generalized. In more recent research, it was shown that the convergence rate can be increased by adaptable control parameters, e. g. mutation rate can be varied during the optimization run. Better optimization results have been achieved. It was shown how control parameters can be varied by self-adapting algorithms. The control parameters are coded within the chromosome to make them independent from the optimization problem. In newer researches, multi-chromosome representations have been used to decompose complex problems into a number of simpler sub-problems. Each part of the problem is represented by a separate chromosome with individual representation. Fitness values have been used to measure how good an individual fits with its environment (target criteria). This paper investigates the effects on GA performance or the optimization results by balancing control parameters to the fitness of a chromosome (chromosome fitness). Further it is investigated how mutation rate can be varied by chromosome fitness and whether this affects the optimization performance of the GA or the optimization results.
References
- Holland, J. H. (1975). Adaption in natural and artificial systems. An introd. analysis with applications to biology, control, and artificial intelligence. Univ. of Michigan, Ann Arbor.
- DeJong, K. A. (1975). A nalysis of the behavior of a class of genetic adaptive. Ph. D. thesis, Univ. of Michigan.
- Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics Vol. 16, No. 1, 122–128.
- Schaffer, J. D. , Caruana, R. A. , Eshelman, L. J. , & Das, R. (1989). A study of control parameters affecting online performance of genetic algorithms for function optimization. Proceedings of the Third International Conference on Genetic Algorithms, 51–60.
- Fogarty, T. C. (1989). Varying the probability of mutation in the genetic algorithm. Proceedings of the 3rd International Conference on Genetic Algorithms, 104–109.
- Hesser, J. ,&Männer, R. (1991). Towards an Optimal Mutation Probability for Genetic Algorithms. Proceedings of 1st workshop : Parallel problem solving from nature, 23–32.
- Bäck, T. (1992). Self-Adaption in Genetic Algorithms. Proceedings of the 1st European Conference on Artificial Life, 263–271.
- Hinterding, R. (1997). Self-adaptation using multi-chromosomes. Proceedings of the IEEE International Conference on Evolutionary Computation, Indianapolis, 87–91.
- Juliff, K. (1993). A multi-chromosome genetic algorithm for pallet loading. Proceedings of the 5th International Conference on Genetic Algorithms, 467–473.
- Cavill, R. , Smith, S. , & Tyrrell, A. (2005). Multi-Chromosomal Genetic Programming. Proceedings of Genetic and Evolutionary Computation Conference (GECCO), Washington D. C. , 1753–1759.
- Davidor, Y. (1991). Genetic Algorithms And Robotics - A Heuristic Strategy For Optimization. World Scientific Publishing Co. Pte. Ltd. , Singapur.
- Bäck, T. (1993). Optimal mutation rates in genetic search. Forrest, S. (Ed. ), Proceedings of the 5th International Conference on Genetic Algorithms, 2–8.
- Ochoa, G. , Harvey, I. , & Buxton, H. (1999). Error thresholds and their relation to optimal mutation rates. Floreano, J. et al. (Eds. ) Proceedings of the Fifth European Conference on Artificial Life Vol. 1674, 54–63.
- Pierrot, H. J. ,&Hinterding, R. (1997). Using multi-chromosomes to solve a simple mixed integer problem. Australian Joint Conference on Artificial Intelligence, 137–146.
- Nissen, V. ,&Biethahn, J. (1999). Ein Beispiel zu stochastischen Optimierung mittels Simulation und einem Genetischen Algorithmus. In Simulation als betriebliche Entscheidungshilfe. State ofthe Art und neuere Entwicklungen; mit 20 Tabellen, J. Biethahn, W. ,Hummeltenberg, B. , Schmidt, P. ,Stähly T. , & Witte, Eds. Physica-Verl, Heidelberg, 108–125.
- Schwefel, H. P. (1975). Evolutionsstrategien und numerische Optimierung. Dissertationsschrift, TechnischeUniversität Berlin.
- Mitchell, M. (1996). An introduction to genetic algorithms. MIT Press, Cambridge, London.
- Bäck, T. (1996). Evolutionary algorithms in theory and practice. Evolution strategies, evolutionary programming, genetic algorithms. Oxford Univ. Press, New York.
- Goldberg, D. E. (2004). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, Mass.
- Pohlheim, H. (2000). Evolutionäre Algorithmen. Verfahren, Operatoren und Hinweise für die Praxis. VDI-Buch. Springer, Berlin.
- Ochoa, G. , Harvey, I. , & Buxton, H. (2000). Optimal Mutation Rates and Selection Pressure in Genetic Algorithms. Proceedings of Genetic and Evolutionary Computation Conference, 315–322.
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