Abstract
This paper provides insight into combining stochastic and deterministic search methods using evolutionary algorithms (EAs) such as evolutionary programming, evolutionary strategies, and genetic algorithms integrated with depth-first search with backtracking, branch and bound, and best-first search algorithms such as A*. An important view of such an integration focuses on both theoretical analysis and experimental evaluation. Included in the discussion is the constraining impact of the “No Free Lunch Theorem” on combined search performance. Also, a variety of combinations of EAs and deterministic combined search methods are proposed along with expert system components. Discussion of anticipated results of such architectures speculates about existence of high performance integrated search environments. A particular successful specific NPC problem environment is presented that employs search metrics for short-term performance evaluation and combined search algorithm selection.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Thomas Bäck. Evolutionary Algorithms in Theory and Practice. Oxford Press, New York, 1996.
P. Cheeseman, B. Kanefsky, and W. M. Taylor. Where the really hard problems are. Proceedings of IJCAI-91, pages 331–337, 1991.
David E. Goldberg, Kalyanmoy Deb, Hillol Kargupta, and Georges Harik. Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In Stephanie Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 56–64, San Mateo, CA, July 1993. Morgan Kaufmann Publishers.
Stuart A. Kauffman. The Origins of Order. Oxford University Press, 1993.
Gary B. Lamont, Scott M. Brown, and George H. Gates Jr. Evolutionary algorithms combined with deterministic search. Technical report, United States Air Force Institute of Technology, 1998.
Roy Pargus, Jennifer Ludwick, and Steven Spoon. Hybrid search algorithms. ACM 1997 Symposium on Applied Computing, 1997.
Judea Pearl. Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison Wesley, 1984.
E. Peter. Ein Beitrag zur wissensbasierten Auswahl und Steuerung von Optimierverfahren. PhD thesis, Universsitat Dortmund, 1991.
Michael J. Quinn. Parallel Computing — theory and practice. McGraw-Hill, 1994.
N. J. Radcilffe and P. D. Surry. Lecture Notes in Computer Science, volume 1000. Springer-Verlag, 1995.
Richard K. Thompson. Fitness landscapes investigated. Master's thesis, University of Montana, 1995.
Edward M. Williams. Modeling Intgelligent Control of Distributed Cooperative Inferencing. PhD thesis, Graduate School of Engineering, Air Force Institute of Technology, 1997.
D.Randall Wilson and Tony R. Martinez. Bias and the probability of generalization. pages 108–114. IEEE Computer Society, 1997.
D. H. Wolpert and W. G. MacReady. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, April 1996.
Weixiong Zhang and Richard E. Korf. Performance of linear-space search algorithms. Artificial Intelligence, 1995.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lamont, G.B., Brown, S.M., Gates, G.H. (1998). Evolutionary algorithms combined with deterministic search. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040803
Download citation
DOI: https://doi.org/10.1007/BFb0040803
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64891-8
Online ISBN: 978-3-540-68515-9
eBook Packages: Springer Book Archive