Abstract
In this paper, we propose a visualization method to grasp the search process and results in the binary-coded genetic algorithm. The representation, the choices of operations, and the associated parameters can each make a major difference to the speed and the quality of the final result. These parameters are decided interactively and very difficult to disentangle their effects. Therefore, we focus on the chromosome structure, the fitness function, the objective function, the termination conditions, and the association among these parameters. We can indicate the most important or optimum parameters in visually. The proposed method is indicated all individuals of the current generation using the pseudo-color. The pixels related a gene of the chromosome are painted the red color when the gene of the chromosome represents ‘1’, and the pixels related to one are painted the blue color when one represents ‘0’. Then the brightness of the chromosome changes by the fitness value, and the hue of the chromosome changes by the objective value. In order to show the effectiveness of the proposed method, we apply the proposed method to the zero-one knapsack problems.
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
Hart, E., Ross, P.: Gavel-A New Tool for Genetic Algorithm Visualization. IEEE Transaction on Evolutionary Computation 5(4), 335–348 (2001)
Eick, S.G., Steffen, J.L., Summer, E.E.: Seesoft-A Tool for Visualization Line Oriented Software Statistics. IEEE Transaction on Software Engineering 18, 957–968 (1992)
Simoes, A., Costa, E.: An Evolutionary Approach to the Sero/One Knapsack Problem: Testing Ideas from Biology. In: Kurkova, V., Steele, N., Neruda, R., Karny, M. (eds.) Proceedings of the Fifth International Conference on Neural Networks and Genetic Algorithms (ICANNGA 2001), Prague, Czech Republic, April 22-25, pp. 236–239. Springer, Heidelberg (2001)
Jones, T.: Crossover, macromutation, and population-based search. In: Eshelman, L. (ed.) Proceedings of the 6th International Conference on Genetic Algorithms, pp. 73–80. Morgan Kaufmann, San Mateo (1995)
Shine, W., Eick, C.: Visualization the evolution of genetic algorithm search processes. In: Proceedings of 1997 IEEE International Conference on Evolutionary Computation, pp. 367–372. IEEE Press, Piscataway (1997)
Olsen, A.L.: Penalty Function and the Knapsack Problem. In: Fogel, D.B. (ed.) Proceedings of the 1st International Conference on Evolutionary Computation 1994, Orlando, FL, pp. 559–564 (1994)
Gordon, V., Bohm, A., Whitley, D.: A Note on the Performance of Genetic Algorithms on Zero-One Knapsack Problems. In: Proceedings of the 9th Symposium on Applied Computing (SAC 1994), Genetic Algorithms and Combinatorial Optimization, Phoenix, Az (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ito, Si., Mitsukura, Y., Miyamura, H.N., Saito, T., Fukumi, M. (2008). A Visualization of Genetic Algorithm Using the Pseudo-color. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-69162-4_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
Online ISBN: 978-3-540-69162-4
eBook Packages: Computer ScienceComputer Science (R0)