Abstract
The preservation of common components has been recently isolated as a beneficial feature of genetic algorithms. One interpretation of this benefit is that the preservation of common components can direct the search process to focus on the most promising parts of the search space. If this advantage can be transferred from genetic algorithms, it may be possible to improve the overall effectiveness of other heuristic search techniques. To identify common components, multiple solutions are required – like those available from a set of parallel searches. Results with simulated annealing and the Traveling Salesman Problem show that the sharing of common components can be an effective method to coordinate parallel search.
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
Boese, K.D.: Models for Iterative Global Optimization. Ph.D. diss., Computer Science Department, University of California at Los Angeles (1996)
Chen, S.: SAGA: Demonstrating the Benefits of Commonality-Based Crossover Operators in Simulated Annealing. Working paper, School of Analytical Studies and Information Technology, York University (2003)
Chen, S., Smith, S.F.: Putting the “Genetics” back into Genetic Algorithms (Reconsidering the Role of Crossover in Hybrid Operators). In: Banzhaf, W., Reeves, C. (eds.) Foundations of Genetic Algorithms, vol. 5. Morgan Kaufmann, San Francisco (1999)
Chen, S., Smith, S.F.: Introducing a New Advantage of Crossover: Commonality-Based Selection. In: GECCO 1999: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, San Francisco (1999)
Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. Wiley, Chichester (1966)
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Hiroyasu, T., Miki, M., Ogura, M.: Parallel Simulated Annealing using Genetic Crossover. In: Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Systems. ACTA Press (2000)
Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press (1975)
Johnson, D.S., McGeoch, L.A.: Experimental Analysis of Heuristics for the STSP. In: Gutin, G., Punnen, A.P. (eds.) The Traveling Salesman Problem and Its Variations. Kluwer Academic Publishers, Dordrecht (2002)
Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)
Mühlenbein, H.: Evolution in Time and Space–The Parallel Genetic Algorithm. In: Rawlins, G. (ed.) Foundations of Genetic Algorithms. Morgan Kaufmann, San Francisco (1991)
Norman, M.G., Moscato, P.: A Competitive and Cooperative Approach to Complex Combinatorial Search, Caltech Concurrent Computation Program, C3P Report 790 (1989)
Radcliffe, N.J.: Forma Analysis and Random Respectful Recombination. In: Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco (1991)
Radcliffe, N.J., Surry, P.D.: Formal memetic algorithms. In: Fogarty, T. (ed.) Evolutionary Computing: AISB Workshop. Springer, Heidelberg (1994)
Reinelt, G.: The Traveling Salesman: Computational Solutions for TSP Applications. Springer, Heidelberg (1994)
Sanvicente, H., Frausto-Solís, J.: MPSA: A Methodology to Parallel Simulated Annealing and its Application to the Traveling Salesman Problem. In: Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence. Springer, Heidelberg (2002)
Schwefel, H.-P.: Numerical Optimization of Computer Models. Wiley, Chichester (1981)
Syswerda, G.: Uniform Crossover in Genetic Algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco (1989)
Wendt, O., König, W.: Cooperative Simulated Annealing: How much cooperation is enough? Technical Report, No. 1997-3, School of Information Systems and Information Economics at Frankfurt University (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, S., Pitt, G. (2005). The Coordination of Parallel Search with Common Components. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_87
Download citation
DOI: https://doi.org/10.1007/11504894_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26551-1
Online ISBN: 978-3-540-31893-4
eBook Packages: Computer ScienceComputer Science (R0)