Abstract
In the literature, the term premature convergence of the entire population is used with the meaning of closing the evolution before reaching the optimal point. It can be emphasized only on a test function with known landscape. If the function landscape is unknown, one can notice the population convergence only. This paper aims to answer to the question: ”how can we influence the control parameters of the genetic algorithm so that the exploration time of the parameter space be longer and the risk of premature convergence be reduced?”. The answer to the above question implies choosing a crossover operator with good performances in the landscape exploration and the use of two performance indicators for the detection of the population convergence. In choosing the control parameters of the genetic algorithm, the fitness function landscape must be taken into consideration.
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References
Booker, L.: Improving Search in Genetic algorithms. In: Davis, L. (ed.) Genetic Algorithms and Simulated Anealing, ch. 5, pp. 61–73. Morgan Kaufman Publishers, Los Altos (1987)
Belea, R., Beldiman, L.: A New Method of Gene Coding For a Genetic Algorithm Designed for Parametric Optimization. In: The Annals of “Dunarea de Jos” University of Galaţi, Fascicle III, pp. 66–71 (2003)
Eshelman, L.J.: The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination. In: Rowlins, G. (ed.) Foundations of genetic Algorithms, pp. 265–283. Morgan Kaufmann Publishers, Los Altos (1991)
Goldberg, D.E.: Genetic Algorithms. Addison Wesley, USA (1991); French translation, Copyright © June 1994, Editions Addison Wesley, France, S. A
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1994)
Rana, S.: Examining the Role of Local Optima and Schema Processing in Genetic Search. PHD Thesis, Colorado State University, Fort Collins, Colorado (1999)
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Belea, R., Caraman, S., Palade, V. (2004). Diagnosing the Population State in a Genetic Algorithm Using Hamming Distance. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_34
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DOI: https://doi.org/10.1007/978-3-540-30134-9_34
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