An Interactive Visualization of Genetic Algorithm on 2-D Graph

An Interactive Visualization of Genetic Algorithm on 2-D Graph

Humera Farooq (Future University, Sudan), Nordin Zakaria (Universiti Teknologi PETRONAS, Malaysia) and Muhammad Tariq Siddique (Future University, Sudan)
DOI: 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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1. Introduction

Cognitive science is an interdisciplinary field that involves the study of human thinking and methods to solve scientific problems. It is consists of several research fields, for example medicine, psychology, education and artificial intelligence. Among these fields Artificial Intelligence (AI) is a sub field of computer science that involves the implementation of practical aspects of human intelligence into computers. Hence cognitive science grew out of the interest in artificial intelligence to investigate techniques for making a bridge between artificial and natural intelligence. Later on Cognitive Informatics (CI) has been proposed as a cutting-edge and profound interdisciplinary field of computer science that investigates the techniques used for processing information in the human brain (Wang, 2003). CI can be related to natural systems (living organisms), and to artificial systems (computational devices) and to the hybrids of both of them, as like assistive technology. According to Wang classification, the key applications of CI can be divided into two categories. The first category uses informatics and computing techniques to solve problems such as memory, learning and reasoning. The second category investigates problems like informatics, computing and software engineering or knowledge.

AI techniques are integrated with CI to enhance the understanding of human behavior and intelligence in solving different problems. Among these techniques the Genetic Algorithm is a dynamic random searching algorithm used to solve the optimization problems. It was invented by Holland (1992) to explain the adaptive process of natural systems (Haroun, 1997). GA is based on the biological methods to produce the solutions (next population). Being a successful meta-heuristic technique, this algorithm is widely used for solving many optimization problems. Over the past few years scientists have taken advantage of visualization techniques to explore the internal process of the GA, termed as the Interactive Genetic Algorithm (IGA). In computer graphics, visualization plays an important part in helping the users to understand the problems graphically (Purchase, Andrienko, Jankun-Kelly, & Ward, 2008). Visualization technology raises the level of understanding multidimensional or high resolution data. Another advantage of using this technology is to reduce the work period. Visualization does not mean only to view the graphical pictures, but it also includes analyzing and interpreting the data.

IGA is a technique to design an environment with human interaction to evaluate solutions. The visualization of the search space not only demonstrates the process of searching with GA towards a fitted solution, but it also interacts with the search space to help GA to search for the best solution in less time. The Interactive Evolutionary Algorithm (IES) was first demonstrated by Dawkins (1989), when he created a visualize tool to model an artwork called bimorphs. A detail survey on using Interactive Evolutionary Computation may be found in Takagi (2001) in which, Takagi divided the interactive evolution by two definitions. The first one is a narrow definition according to which human evaluation is used as the fitness value for an optimized solution, and the second is a broad definition according to which the optimized solution is obtained from a human-machine interface.

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