Abstract
Approximation algorithms are often employed on hard optimization problems due to the vastness of the search spaces. Many approximation methods, such as evolutionary search, are often indeterminate and tend to converge to solutions that vary with each search attempt. If multiple search instances are executed, then the wisdom among the crowd of stochastic outcomes can be exploited by aggregating them to form a new solution that surpasses any individual result. Wisdom of artificial crowds (WoAC), which is inspired by the wisdom of crowds phenomenon, is a post-processing metaheuristic that performs this function. The aggregation method of WoAC is instrumental in producing results that consistently outperform the best individual. This paper extends the contributions of existing work on WoAC by investigating the performance of several aggregation methods. Specifically, existing and newly proposed WoAC aggregation methods are used to synthesize parallel genetic algorithm (GA) searches on a series of traveling salesman problems (TSPs), and the performance of each approach is compared. Our proposed method of weighting the input of crowd members and incrementally increasing the crowd size is shown to improve the chances of finding a solution that is superior to the best individual solution by 51% when compared to previous methods.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Yampolskiy, R.V., El-Barkouky, A.: Wisdom of artificial crowds algorithm for solving NP-hard problems. International Journal of Bio-Inspired Computation 3(6), 358–369 (2011)
Collet, P., Rennard, J.-P.: Stochastic optimization algorithms (2007). arXiv preprint arXiv:0704.3780
Hoos, H.H., Sttzle, T.: Stochastic search algorithms, vol. 156. Springer (2007)
Kautz, H.A., Sabharwal, A., Selman, B.: Incomplete Algorithms. Handbook of Satisfiability 185, 185–204 (2009)
Yampolskiy, R.V., Ashby, L., Hassan, L.: Wisdom of Artificial Crowds - A Metaheuristic Algorithm for Optimization. Journal of Intelligent Learning Systems and Applications 4, 98 (2012)
Surowiecki, J.: The wisdom of crowds. Random House LLC (2005)
Yi, S.K.M., Steyvers, M., Lee, M.D., Dry, M.: Wisdom of the Crowds in Traveling Salesman Problems. Memory and Cognition 39, 914–992 (2011)
Hoshen, Y., Ben-Artzi, G., Peleg, S.: Wisdom of the crowd in egocentric video curation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 587–593, June 23–28, 2014
Jiangbo, Y., Kian Hsiang, L., Oran, A., Jaillet, P.: Hierarchical Bayesian nonparametric approach to modeling and learning the wisdom of crowds of urban traffic route planning agents. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 478–485, December 4–7, 2012
Kittur, A., Kraut, R.E.: Harnessing the wisdom of crowds in wikipedia: quality through coordination. Paper presented at the Proceedings of the 2008 ACM conference on Computer supported cooperative work, San Diego, CA, USA
Moore, T., Clayton, R.C.: Evaluating the wisdom of crowds in assessing phishing websites. In: Tsudik, G. (ed.) FC 2008. LNCS, vol. 5143, pp. 16–30. Springer, Heidelberg (2008)
Velic, M., Grzinic, T., Padavic, I.: Wisdom of crowds algorithm for stock market predictions. In: Proceedings of the International Conference on Information Technology Interfaces, ITI, pp. 137–144 (2013)
Ashby, L.H., Yampolskiy, R.V.: Genetic algorithm and wisdom of artificial crowds algorithm applied to light up. In: 2011 16th International Conference on Computer Games (CGAMES), pp. 27–32, July 27–30, 2011
Hughes, R., Yampolskiy, R.V.: Solving Sudoku Puzzles with Wisdom of Artificial Crowds. International Journal of Intelligent Games and Simulation 7(1), 6 (2013)
Khalifa, A.B., Yampolskiy, R.V.: GA with Wisdom of Artificial Crowds for Solving Mastermind Satisfiability Problem. International Journal of Intelligent Games and Simulation 6(2), 6 (2011)
Port, A.C., Yampolskiy, R.V.: Using a GA and wisdom of artificial crowds to solve solitaire battleship puzzles. In: 2012 17th International Conference on Computer Games (CGAMES), pp. 25–29, July 30, 2012-August 1, 2012
Puuronen, S., Terziyan, V., Tsymbal, A.: A dynamic integration algorithm for an ensemble of classifiers. In: Ra, Z., Skowron, A. (eds.) Foundations of Intelligent Systems. Lecture Notes in Computer Science, vol. 1609, pp. 592–600. Springer, Berlin Heidelberg (1999)
Wagner, C., Ayoung, S.: The wisdom of crowds: impact of collective size and expertise transfer on collective performance. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 594–603, January 6–9, 2014
Concorde TSP Solver. http://www.math.uwaterloo.ca/tsp/concorde/index.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lowrance, C.J., Abdelwahab, O., Yampolskiy, R.V. (2015). Evolution of a Metaheuristic for Aggregating Wisdom from Artificial Crowds. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-23485-4_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23484-7
Online ISBN: 978-3-319-23485-4
eBook Packages: Computer ScienceComputer Science (R0)