Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Schemata evolution and building blocks
Evolutionary Computation
Concurrent application of genetic algorithm in pattern recognition
Design and application of hybrid intelligent systems
| Hi-index | 0.00 |
This paper presents the use of a genetic algorithm based heuristic measure for quantifying perceptable similarity of visual patterns by the example of Kirlian photographs. Measuring similarity of such patterns can be considered a trade-off between quantifying strong similarity for some parts of the pattern, and the neglection of the accidental abscense of other pattern parts as well. For this reason, the use of a dynamic measure instead of a static one is motivated. Due to their well-known schemata processing abilities, genetic algorithm seem to be a good choice for "performing" such a measurement. The results obtained from a real set of Kirlian images shows that the ranking of the proposed heuristic measure is able to reflect the apparent visual similarity ranking of Kirlian patterns.