Abstract
We introduce, analyze, and experimentally examine co-solva-bility, an ability of a solution to solve a pair of fitness cases (tests). Based on this concept, we devise a co-solvability fitness function that makes solutions compete for rewards granted for solving pairs of tests, in a way analogous to implicit fitness sharing. We prove that co-solvability fitness function is by definition synergistic and imposes selection pressure which is qualitatively different from that of standard fitness function or implicit fitness sharing. The results of experimental verification on eight genetic programming tasks demonstrate that evolutionary runs driven by co-solvability fitness function usually converge faster to well-performing solutions and are more likely to reach global optima.
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.
Similar content being viewed by others
References
Bucci, A., Pollack, J.B., de Jong, E.: Automated extraction of problem structure. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 501–512. Springer, Heidelberg (2004)
de Jong, E.D., Pollack, J.B.: Ideal Evaluation from Coevolution. Evolutionary Computation 12(2), 159–192 (Summer 2004)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization. John Wiley & Sons, Chichester (2001)
Goldberg, D.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)
Knowles, J.D., Watson, R.A., Corne, D.: Reducing local optima in single-objective problems by multi-objectivization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 269–283. Springer, Heidelberg (2001)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Lasarczyk, C.W.G., Dittrich, P., Banzhaf, W.: Dynamic subset selection based on a fitness case topology. Evolutionary Computation 12(2), 223–242 (Summer 2004)
Luke, S.: ECJ evolutionary computation system (2002), http://cs.gmu.edu/eclab/projects/ecj/
McKay, R.I.B.: Committee learning of partial functions in fitness-shared genetic programming. In: 26th Annual Confjerence of the IEEE Third Asia-Pacific Conference on Simulated Evolution and Learning 2000, Industrial Electronics Society, IECON 2000, Nagoya, Japan, October 22-28, vol. 4, pp. 2861–2866. IEEE Press, Los Alamitos (2000)
McKay, R.I.B.: Fitness sharing in genetic programming. In: Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.-G. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), Las Vegas, Nevada, USA, July 10-12, pp. 435–442. Morgan Kaufmann, San Francisco (2000)
Smith, R., Forrest, S., Perelson, A.: Searching for diverse, cooperative populations with genetic algorithms. Evolutionary Computation 1(2) (1993)
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
Krawiec, K., Lichocki, P. (2010). Using Co-solvability to Model and Exploit Synergetic Effects in Evolution. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-15871-1_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15870-4
Online ISBN: 978-3-642-15871-1
eBook Packages: Computer ScienceComputer Science (R0)