Seesoft-A Tool for Visualizing Line Oriented Software Statistics
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
A note on the performance of genetic algorithms on zero-one knapsack problems
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Crossover, Macromutationand, and Population-Based Search
Proceedings of the 6th International Conference on Genetic Algorithms
GAVEL - a new tool for genetic algorithm visualization
IEEE Transactions on Evolutionary Computation
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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.