Abstract
An inherent assumption in many search techniques is that information from existing solution(s) can help guide the search process to find better solutions. For example, memetic algorithms can use information from existing local optima to effectively explore a globally convex search space, and genetic algorithms assemble new solution candidates from existing solution components. At the extreme, the quality of a random solution may even be used to identify promising areas of the search space to explore. The best of several random solutions can be viewed as a “smart” start point for a greedy search technique, and the benefits of “smart” start points are demonstrated on several benchmark and real-world optimization problems. Although limitations exist, “smart” start points are most likely to be useful on continuous domain problems that have expensive solution evaluations.
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
Beyer, H.-G., Schwefel, H.-P.: Evolution Strategies: A comprehensive introduction. Natural Computing 1, 3–52 (2002)
Boese, K.D.: Models for Iterative Global Optimization. Ph.D. diss., Computer Science Department, University of California at Los Angeles (1996)
Brünger, A.T., Krukowski, A., Erickson, J.W.: Slow-cooling protocols for crystallographic refinement by simulated annealing. Acta Crystallographica A46, 585–593 (1990)
Chen, S., Razzaqi, S., Lupien, V.: An Evolution Strategy for Improving the Design of Phased Array Transducers. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, pp. 2859–2863. IEEE Press, Los Alamitos (2006)
Chen, S., Razzaqi, S., Lupien, V.: Towards the Automated Design of Phased Array Ultrasonic Transducers – Using Particle Swarms to find "Smart" Start Points. In: Okuno, H.G., Moonis, A. (eds.) Proceedings of the 20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. LNCS, vol. 4570, pp. 313–323. Springer, Heidelberg (2007)
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 5, Morgan Kaufmann, San Francisco (1999)
Hassan, W., Vensel, F., Knowles, B., Lupien, V.: Improved Titanium Billet Inspection Sensitivity through Optimized Phased Array Design, Part II: Experimental Validation and Comparative Study with Multizone. In: AIP Conference Proceedings. Quantitative Nondestructive Evaluation, vol. 820, pp. 861–868. AIP (2006)
Hendtlass, T.: WoSP: A Multi-Optima Particle Swarm Algorithm. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 1, pp. 727–734. IEEE Press, Los Alamitos (2005)
Johnson, D.S., McGeoch, L.A.: The Traveling Salesman Problem: A Case Study in Local Optimization. In: Aarts, E.H.L., Lenstra, J.K. (eds.) Local Search in Combinatorial Optimization, pp. 215–310. John Wiley and Sons, Chichester (1997)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE Press, Los Alamitos (1995)
Lupien, V., Hassan, W., Dumas, P.: Improved Titanium Billet Inspection Sensitivity through Optimized Phased Array Design, Part I: Design Technique, Modelling and Simulation. In: AIP Conference Proceedings. Quantitative Nondestructive Evaluation, vol. 820, pp. 853–860. AIP (2006)
Merz, P., Freisleben, B.: Memetic Algorithms for the Traveling Salesman Problem. Complex Systems 13(4), 297–345 (2001)
Mühlenbein, H.: Evolution in Time and Space–The Parallel Genetic Algorithm. In: Raw-lins, G. (ed.) Foundations of Genetic Algorithms, Morgan Kaufmann, San Francisco (1991)
Radcliffe, N.J., Surry, P.D.: Formal memetic algorithms. In: Fogarty, T.C. (ed.) Evolutionary Computing. LNCS, vol. 865, pp. 1–16. Springer, Heidelberg (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, S., Miura, K., Razzaqi, S. (2007). Analyzing the Role of “Smart” Start Points in Coarse Search-Greedy Search. In: Randall, M., Abbass, H.A., Wiles, J. (eds) Progress in Artificial Life. ACAL 2007. Lecture Notes in Computer Science(), vol 4828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76931-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-76931-6_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76930-9
Online ISBN: 978-3-540-76931-6
eBook Packages: Computer ScienceComputer Science (R0)