Fundamentals of digital image processing
Fundamentals of digital image processing
Model-based image interpretation using genetic algorithms
Image and Vision Computing - Special issue: BMVC 1991
Pattern Recognition Letters - Special issue on genetic algorithms
Genetic optimisation of the image feature extraction process
Pattern Recognition Letters
Application of neural networks and genetic algorithms in the classification of endothelial cells
Pattern Recognition Letters - special issue on pattern recognition in practice V
Genetic operators for hierarchical graph clustering
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A genetic algorithm approach to Chinese handwriting normalization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
A modified Genetic Algorithm (GA) based search strategy is presented here that is computationally more efficient than the conventional GA. Here the idea is to start a GA with the chromosomes of small length. Such chromosomes represent possible solutions with coarse resolution. A finite space around the position of solution in the first stage is subject to the GA at the second stage. Since this space is much smaller than the original search space, chromosomes of same length now represent finer resolution. In this way, the search progresses from coarse to fine solution in a cascaded manner. Since chromosomes of small size are used at each stage, the overall approach becomes computationally more efficient than a single stage algorithm with the same degree of final resolution. Also, since at the lower stage we work on low resolution, the algorithm can avoid local spurious extrema. The effectiveness of the proposed GA has been demonstrated for the optimization of some synthetic functions and on pattern recognition problems namely dot pattern matching and object matching with edge map.