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An Interactive Visualization of Genetic Algorithm on 2-D Graph

An Interactive Visualization of Genetic Algorithm on 2-D Graph

Humera Farooq, Nordin Zakaria, Muhammad Tariq Siddique
Copyright: © 2012 |Volume: 4 |Issue: 1 |Pages: 21
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781466614192|DOI: 10.4018/jssci.2012010102
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MLA

Farooq, Humera, et al. "An Interactive Visualization of Genetic Algorithm on 2-D Graph." IJSSCI vol.4, no.1 2012: pp.34-54. http://doi.org/10.4018/jssci.2012010102

APA

Farooq, H., Zakaria, N., & Siddique, M. T. (2012). An Interactive Visualization of Genetic Algorithm on 2-D Graph. International Journal of Software Science and Computational Intelligence (IJSSCI), 4(1), 34-54. http://doi.org/10.4018/jssci.2012010102

Chicago

Farooq, Humera, Nordin Zakaria, and Muhammad Tariq Siddique. "An Interactive Visualization of Genetic Algorithm on 2-D Graph," International Journal of Software Science and Computational Intelligence (IJSSCI) 4, no.1: 34-54. http://doi.org/10.4018/jssci.2012010102

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Abstract

The visualization of search space makes it easy to understand the behavior of the Genetic Algorithm (GA). The authors propose a novel way for representation of multidimensional search space of the GA using 2-D graph. This is carried out based on the gene values of the current generation, and human intervention is only required after several generations. The main contribution of this research is to propose an approach to visualize the GA search data and improve the searching process of the GA with human’s intention in different generations. Besides the selection of best individual or parents for the next generation, interference of human is required to propose a new individual in the search space. Active human intervention leads to a faster searching, resulting in less user fatigue. The experiments were carried out by evolving the parameters to derive the rules for a Parametric L-System. These rules are then used to model the growth process of branching structures in 3-D space. The experiments were conducted to evaluate the ability of the proposed approach to converge to optimized solution as compared to the Simple Genetic Algorithm (SGA).

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