Abstract
Parallel Genetic Algorithms (PGA) have been implemented in the past largely on parallel computers, and more recently on serial PCs. PGAs have been used successfully in solving many difficult optimization tasks. To gain further insight into the state and progress of the algorithm, we often need to extract useful information from the large amount of data generated from a PGA run, but this can be a difficult task. Many of the current PGA implementations often have no capability of visualizing an evolving GA population dynamically during execution time. In this paper, we describe an implementation of a finegrained parallel GA using Swarm, a multi-agent simulation tool originally developed at the Santa Fe institute. The PGA model developed is capable of visualizing dynamically the performance of an evolving GA population with plotted graphs on model parameter values in real time. This implementation also allows modification of some model parameter values during an optimization run, therefore offers advantages over many existing PGA implementations. We demonstrate the usefulness of the visualization techniques used in this PGA implementation using two optimization examples.
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
Holland, J.H. (1975) Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.
Grefenstette, J.J., (ed.,) (1987) Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Cambridge, MA, July 28-31, 1987.
Cantu-Paz E. (1997) “A Survey of Parallel Genetic Algorithms”, IlliGAL Report No. 97003, May 1997, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana-Champaign.
Routen, T.W. (1994). Techniques for the Visualization of Genetic Algorithms. In Proceedings of The First IEEE Conference on Evolutionary Computation, Piscataway, New Jersey, USA: IEEE Service Center, Vol. II, pp.846–851.
Pohlheim, H. (1999). Visualization of Evolutionary Algorithms-Set of Standard Techniques and Multidimentional Visualization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’99). San Francisco, CA: Morgan Kaufmann Publishers, pp.533–540.
Stefansson, B. (1997) “Swarm: An Object Oriented Simulation Platform Applied to Markets and Organizations”, Evolutionary Programming VI, Lecture Notes in Computer Science, edited by Angeline, P., Reynolds, R., and Eberhart, R. Vol.1213, Springer-Verlag, New York.
Minar, N., Burkhart, R., Langton, C., and Askenazi, M. (1996) “The Swarm Simulation System-A Toolkit for Building Multi-Agent Systems”, Santa Fe Institute Working Paper 96-06-042, Santa Fe, NM.
Stefansson, B.(1998) “Agent Based Modeling in Swarm”, Lecture notes, UCLA Political Science.
Burkhart, R. (1997) “Schedules of Activity in the Swarm Simulation System”, Position Paper for OOPSLA’s 97 Wrokshop on OO Behavioral Semantics.
Goldberg, D. (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.
Kirley, M., Li, X. and Green, D.G. (1998) “An investigation of a Cellular Genetic Algorithm that mimics evolution in a landscape”, Lecture Notes in Artificial Intelligence, edited by B. McKay, et al., vol: 1585.
Merelo, J.J., GeNeura and Swarm Teams (1997) “Breeder user’s and programmer’s Manual”, Technical report, http://www.swarm.org/community-contrib.html.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, X. (2001). Visualization of a Parallel Genetic Algorithm in Real Time. In: Liu, J., Yuen, P.C., Li, Ch., Ng, J., Ishida, T. (eds) Active Media Technology. AMT 2001. Lecture Notes in Computer Science, vol 2252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45336-9_39
Download citation
DOI: https://doi.org/10.1007/3-540-45336-9_39
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43035-3
Online ISBN: 978-3-540-45336-9
eBook Packages: Springer Book Archive