Abstract
We propose a novel Genetic Algorithm which we call a Parameter-free Genetic Algorithm (PfGA) inspired by the “disparity theory of evolution”. The idea of the theory is based on different mutation rates in double strands of DNA. Furthermore, its idea is extended to a very compact and fast adaptive search algorithm accelerating its evolution based on the variable-size of population taking a dynamic but delicate balance between exploration (i.e., global search) and exploitation (i.e., local search). The PfGA is not only simple and robust, but also does not need to set almost all genetic parameters in advance that need to be set up in other Genetic Algorithms. To verify the effectiveness of the PfGA, we compared its results with those on the first Internatinal Contenst on Evolutionary Optimization at ICEC'96 using some recent function optimization problems. A parallel and distributed PfGA architecture is being investigated as an extension of this work, some preliminary results of which are shown.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Charles Darwin, “On the Origin of Species by Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life,” London, John Murray, 1859.
J.H.Holland, “Adaptation in Natural and Artificial System,” The University of Michigan Press, 1975.
D.E. Goldberg, “Genetic Algorithm in Search, Optimization, and Machine Learning,” Addison Wesley, 1989.
De Jong, “An Analysis of the Behavior: a Class of Genetic Adaptive Systems,” Doctoral dissertation, University of Michigan, 1975.
M. Furusawa and H. Doi, “Promotion of Evolution: Disparity in the Frequency of Strand-specific Misreading Between the Lagging and Leading DNA Strands Enhances Disproportionate Accumulation of Mutations,” J. theor. Biol., vol. 157, pp 127–133, 1992.
K. Wada, H. Doi, S. Tanaka, Y. Wada, and M. Furusawa, “A Neo-Darwinian Algorithm: Asymmetrical Mutations due to Semiconservative DNA-type replication Promote Evolution,” Proc. Natl. Acad. Sci., USA, vol. 90, pp 11934–11938, Dec. 1993.
T. Baeck, D. Fogel and Z. Michalewicz (Eds.), “Handbook of evolutionary computation,” New York: Oxford University Press, 1997.
The Organising Committee: H.Bersini, M.Doringo, S.Langerman, G.Seront, L.Gambardella, “Results of the First International Contest on Evolutionary Optimization (1st ICEO),” 1996 IEEE Int. Conf. on Evolutionary Computation (ICEC'96), pp611–615, 1996.
G. Bilchev and I. Parmee, “Inductive Search,” 1996 IEEE Int. Conf. on Evolutionary Computation (ICEC'96), pp832–836, 1996.
D.Whitley, K.Mathias, S.Rana and J.Dzubera, “Building Better Test Functions,” Proc. of the Sixth Int. Conf. on Genetic Algorithms, pp 239–246, Morgan Kaufmann, 1995.
Al Geist, Adam Beguelin, Jack Dongarra, Weicheng Jiang, Robert Manchek and Vaidy Sunderam, “PVM UER'S GUIDE AND REFERENCE MANUAL,” 1993.5.
R.Hinterding, Z.Michalewicz and A.E.Eiben, “Adaptation in Evolutionary Computation: A Survey,” Proc. of the 1997 IEEE Int. Conf. on Evolutionary Computation, pp65–69, 1997.
T.C.Belding, “The Distributed Genetic Algorithm Revisited,” Proc. of the Sixth Int. Conf. on Genetic Algorithms, pp114–121, Morgan Kaufmann, 1995.
E.C-Paz and D.E.Goldberg, “Predicting Speedups of Idealized Bounding Cases of Parallel Genetic Algorithms,” Proc. of the Seventh Int. Conf. on Genetic Algorithms, pp 113–126, Morgan Kaufmann, 1997.
I.K.Evans, “Embracing Premature Convergence: The Hypergamous Parallel Genetic Algorithm,” Proc. of the Int. Conf. on Evolutionary Computation '98, pp621–626, 1998.
T.Maruyama, T.Hirose and A.Konagaya, “A Fine-Grained Parallel Genetic Algorithm for Distributed Parallel System,” Proc. of the Fifth Int. Conf. on Genetic Algorithms, pp184–190, Morgan Kaufmann, 1993.
S. Kizu, H. Sawai, and T. Endo, “Parameter-free Genetic Algorithm: GA without Setting Genetic Parameters,” Proc. of the 1997 Int. Symp. on Nonlinear Theory and its Applications, vol.2 of 2, pp1273–1276, Dec. 1997.
H. Sawai, S. Kizu, and T. Endo, “Parameter-free Genetic Algorithm (PfGA)”, Trans. IEICE, Japan, vol. J81-D-II, No.2, pp450–452, Feb. 1998 (in Japanese).
H. Sawai, S. Kizu, and T. Endo, “Performance Comparison of the Parameter-free Genetic Algorithm (PfGA) with Steady-state GA”, Trans. IEICE, Japan, vol. J81-D-II, No.6, pp1455–1459, June. 1998 (in Japanese).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sawai, H., Kizu, S. (1998). Parameter-free Genetic Algorithm inspired by “disparity theory of evolution”. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056912
Download citation
DOI: https://doi.org/10.1007/BFb0056912
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65078-2
Online ISBN: 978-3-540-49672-4
eBook Packages: Springer Book Archive