Paper Info
Title
A Visualization of Genetic Algorithm Using the Pseudo-color
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.
Year
DOI
Venue
2007
10.1007/978-3-540-69162-4_46
ICONIP
Keywords
Field
DocType
objective value,blue color,chromosome structure,genetic algorithm,fitness function,red color,chromosome change,objective function,visualization method,fitness value,visualization,knapsack problem
Chromosome (genetic algorithm),GRASP,Pattern recognition,Computer science,Visualization,Hue,Fitness function,Pixel,Artificial intelligence,Knapsack problem,Machine learning,Genetic algorithm
Conference
Volume
ISSN
Citations 
4985
0302-9743
4
PageRank 
References 
Authors
0.40
6
5
Name
Order
Citations
PageRank
Shin-ichi Ito11012.32
Yasue Mitsukura216347.48
Hiroko Nakamura Miyamura3525.94
Takafumi Saito418023.77
Minoru Fukumi514649.05