Adaptive approaches to parameter control in genetic algorithms and genetic programming

• Autorzy: Spalek J. , Gregor M.
• Języki publikacji: EN

Abstrakty

EN

The paper concerns the application of Genetic Algorithms and Genetic Programming to complex tasks such as automated design of control systems, where the space of solutions is non-trivial and may contain discontinuities. Several adaptive mechanisms for control of the search algorithm's parameters are proposed, investigated and compared to each other. It is shown that the proposed mechanisms are useful in preventing the search from getting trapped in local extremes of the fitness landscape.

Słowa kluczowe

EN

adaptive approach; genetic algorithms; genetic programming

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2011
• Tom: Vol. 7, no 1
• Strony: 38--56
• Opis fizyczny: Bibliogr. 12 poz., fig.

Twórcy

autor

Spalek J.

• Department of Control and Information Systems, Faculty of Electrical Engineering, University of Žilina, Univerzitná 1, 010 26 Žilina, Slovak Republic

autor

Gregor M.

• Department of Control and Information Systems, Faculty of Electrical Engineering, University of Žilina, Univerzitná 1, 010 26 Žilina, Slovak Republic

Bibliografia

• 1. HYNEK, J.: Genetické algoritmy a genetické programování. Grada Publishing, a. s. Praha, 2008. ISBN 978-80-7300-218-3 [In Czech.]
• 2. ALBA, E., COTTA, C.: Evolutionary Algorithms. 2004. http://www.lcc.uma.es/%7Eccottap/papers/eas.pdf
• 3. MITCHELL, M.: An Introduction to Genetic Algorithms. A Bradford Book The MIT Press. Cambridge, Massachusetts, 1999. ISBN 0−262−13316−4
• 4. SADJADI, F. A.: Comparison of fitness scaling functions in genetic algorithms with applications to optical processing. Optical Information Systems II, Proceedings of SPIE: Vol. 5557, 2004.
• 5. BANERJEE, A.: Fitness Scaling. [quot. 11-20-2010]. http://www.cse.unr.edu/~banerjee/scaling.htm
• 6. LAROSE, D. T.: Data Mining Methods and Models. John Wiley & Sons. New Jersey, 2006. ISBN 978-04-7166-656-1
• 7. BUSETTI, F.: Genetic algorithms overview. 2001. http://www.vit.ac.in/academicresearch/res701/RES701DUMP%5CEvolutionary%20Algorithms%5Cgaweb.pdf
• 8. EIBEN, Á. E., ROBERT, H., MICHALEWICZ, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions of Evolutionary Computation: 3, 1999. http://www.gpa.etsmtl.ca/cours/sys843/pdf/Eiben1999.pdf
• 9. THIERENS, D.: Adaptive mutation rate control schemes in genetic algorithms. Proceedings of the 2002 IEEE World Congress on Computational Intelligence:00Congress on Evolutionary Computation, 2002. http://dynamics.org/~altenber/UH_ICS/EC_REFS/GP_REFS/CEC/2002/GP_WCCI_2002/7315.PDF
• 10. SRINIVAS, M., PATNAIK, L. M.: Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics: 24, 1994. http://eprints.iisc.ernet.in/archive/00006971/02/adaptive.pdf
• 11.KOZA, J. R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press. Cambridge, Massachusetts, 1998. ISBN 0-262-11170-5
• 12.MONTANA, D. J.: Strongly Typed Genetic Programming. Evolutionary Computation: 3, 1995. http://vishnu.bbn.com/papers/stgp.pdf