Abstract
Genetic algorithms (GAs) share a common weakness with most other metaheuristics: Candidate solutions are in general revisited multiple times, lowering diversity and wasting precious CPU time. We propose a complete solution archive based on a special binary trie structure for GAs with binary representations that efficiently stores all evaluated solutions during the heuristic search. Solutions that would later be revisited are detected and effectively transformed into similar yet unconsidered candidate solutions. The archive’s relevant insert, find, and transform operations all run in time O(l) where l is the length of the solution representation. From a theoretical point of view, the archive turns the GA into a complete algorithm with a clear termination condition and bounded run time. Computational results are presented for Royal Road functions and NK landscapes, indicating the practical advantages.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aho, A.V., Hopcroft, J.E., Ullman, J.D.: Data Structures and Algorithms. Addison-Wesley, Reading (1985)
Battiti, R., Tecchiolli, G.: The reactive tabu search. ORSA Journal on Computing 6, 126–140 (1994)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2008)
Gantovnik, V.B., Anderson-Cook, C.M., Grdal, Z., Watson, L.T.: A genetic algorithm with memory for mixed discrete-continuous design optimization. Computers and Structures 81, 2003–2009 (2003)
Glover, F.: Tabu search - part II. ORSA Journal on Computing 2(1), 4–32 (1990)
Gusfield, D.: Algorithms on Strings, Trees, and Sequences. Cambridge University Press, Cambridge (1997)
Holland, J.: Adaptation In Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, Oxford (1993)
Knuth, D.E.: The Art of Computer Programming Vol. III: Sorting and Searching. Addison-Wesley, Reading (1973)
Kratica, J.: Improving performances of the genetic algorithm by caching. Computers and Artificial Intelligence 18(3), 271–283 (1999)
Louis, S.J., Li, G.: Combining robot control strategies using genetic algorithms with memory. In: Angeline, P.J., McDonnell, J.R., Reynolds, R.G., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213, pp. 431–442. Springer, Heidelberg (1997)
Mauldin, M.L.: Maintaining diversity in genetic search. In: AAAI, pp. 247–250 (1984)
Mitchell, M., Forrest, S., Holland, J.H.: The royal road for genetic algorithms: Fitness landscapes and GA performance. In: Varela, F.J., Bourgine, P. (eds.) Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pp. 245–254. MIT Press, Cambridge (1992)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1992)
Raidl, G.R., Gottlieb, J.: On the importance of phenotypic duplicate elimination in decoder-based evolutionary algorithms. In: Brave, S., Wu, A.S. (eds.) Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference, Orlando, FL, pp. 204–211 (1999)
Ronald, S.: Complex systems: Mechanism of adaption. In: Stonier, R., Yu, X.H. (eds.) Complex Systems: Mechanism of Adaptation, pp. 133–140. IOS Press, Amsterdam (1994)
Ronald, S.: Duplicate genotypes in a genetic algorithm. In: Fogel, D.B., Schwefel, H.P., Bck, T., Yao, X. (eds.) IEEE World Congress on Computational Intelligence (WCCI 1998), pp. 793–798 (1998)
Sramko, A.: Enhancing a genetic algorithm by a complete solution archive based on a trie data structure. Master’s thesis, Vienna University of Technology, Institute of Computer Graphic s and Algorithms, Vienna, Austria (February 2009)
Yuen, S.Y., Chow, C.K.: A non-revisiting genetic algorithm. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 4583–4590. IEEE Press, Los Alamitos (2007)
Zaubzer, S.: A complete archive genetic algorithm for the multidimensional knapsack problem. Master’s thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria (May 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raidl, G.R., Hu, B. (2010). Enhancing Genetic Algorithms by a Trie-Based Complete Solution Archive. In: Cowling, P., Merz, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2010. Lecture Notes in Computer Science, vol 6022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12139-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-12139-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12138-8
Online ISBN: 978-3-642-12139-5
eBook Packages: Computer ScienceComputer Science (R0)