Abstract
In Darwinian-type binary-coded genetic algo- rithms, there exist bit importance and convergent order of bit. Inspired from this phenomenon, we propose an evolutionary algorithm called Unit Bit Importance Evolutionary Algorithm (UBIEA). Compared with Darwinian-type genetic algorithms, UBIEA completely abandons commonly used genetic operators such as mutation and crossover. Its main idea is that: detecting bit importance, speeding up the convergence of important bits and maintaining the diversity of unimportant bits. Although our theoretic analyses are based on the assumption that all bits are independent and have different salience, UBIEA perhaps is usable in a larger framework which can be verified by numerical experiments.
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References
Holland JH (1992) Adaptation in nature and artificial system. MIT Press, Cambridge
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading
Fogel DB (1995) Evolutionary computation: toward a new philosophy of machine intelligence. IEEE Press, Piscatway, New Jersey
Larrañaga P, Lozano JA (2002) Estimation of distribution algoriothms. Kluwer, Dordrecht
Baluja S (1994) Population-based incremental learning: a method for intergrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, Carnegie Mellon University, Pittsburg
Muhlenbein H, Mahnig T (1999) FDA – a scalable evolutionary algorithm of the optimization of additively decomposed functions. Evol Comput 7(4): 353–376
Harik G, Lobo FG, Goldberg DE (1998) The compacted genetic algorithms. In: Proceedings of the IEEE conference on evolutionary computation, pp 523–528
Muhlenbein H (1998) The equation for response to selection and its use for prediction. Evol Comput 5(3): 303–346
Muhlenbein H, Mahing T (2000) Evolutionary algorithms: from recombination to search distributions. In: Kallel L, Nandts B, Rogers A (eds) Theoretical aspects of evolutionary computation. Springer, Natural Computing, pp 137–176
Rudnick M (1992) Genetic algorithms and fitness variance with an application to automated design of artificial nueral networks. PhD thesis, Oregon Graduate Institute of Science and Technology.
Thierens D, Goldberg DE, Pereira AG (1998) Domino convergence, drift, and the temporal-salience structure of problems. In: Proceedings of the 1998 International conference on evolutionary computation, pp 535–540
Michalski RS (2000) Learnable evolutionaty model: evolutionary processes guided by machine learning. Kluwer, Dordrecht, pp 9–40
Mühlenbein H, Mahnig T (2001) Comparing the adaptive Boltzmann selection schedule SDS to truncation selection systems. In: Proceedings of the Third International Symposium on Adaptive Systems, Havanna, pp 121–128
Hong Y, Ren Q, Zeng J (2005) Genetic drift in univariate marginal distribution algorithm. In: Genetic and evolutionary computation conference (GECCO2005), Washington, USA, pp 735–736
Shapiro L (2005) Drift and scaling in estimation of distribution algorithms. Evol Comput 13(1): 99–123
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Hong, Y., Ren, Q. & Zeng, J. Unit Bit Importance Evolutionary Algorithm. Soft Comput 11, 115–122 (2007). https://doi.org/10.1007/s00500-006-0057-7
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DOI: https://doi.org/10.1007/s00500-006-0057-7