Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Automatic PCB inspection algorithms: a survey
Computer Vision and Image Understanding
Analysis of speciation and niching in the multi-niche crowding GA
Theoretical Computer Science - Special issue on evolutionary computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Fast template matching using bounded partial correlation
Machine Vision and Applications
A sequential niche technique for multimodal function optimization
Evolutionary Computation
An agent-based collaborative evolutionary model for multimodal optimization
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Defects Identification in Textile by Means of Artificial Neural Networks
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Dynamic Selection of a Video Content Adaptation Strategy from a Pareto Front
The Computer Journal
Using multiple genetic algorithms to generate radar point-scatterermodels
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A novel evolutionary algorithm inspired by the states of matter for template matching
Expert Systems with Applications: An International Journal
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This paper describes an improved genetic algorithm (GA) using the notion of species in order to solve an embroidery inspection problem. This inspection problem is actually a multiple template matching problem which can be formulated as a multimodal optimization problem. In many cases, the run time of the multiple template matching problem is dominated by repeating the similarity calculations and moving the templates over the source image. To cope with this problem, the proposed species based genetic algorithm (SbGA) is capable to determine its neighborhood best values for solving multimodal optimization problems. The SbGA has been statistically tested and compared with other genetic algorithms on a number of benchmark functions. After proving its effectiveness, it is integrated with multi-template matching method, namely SbGA-MTM method to solve the embroidery inspection problem. Furthermore, the notion of bounded partial correlation (BPC) is also adopted as an acceleration strategy, which enhances the overall efficiency. Experimental results indicate that the SbGA-MTM method is proven to solve the inspection problem efficiently and effectively. With the proposed method, the embroidered patterns can be identified and checked automatically.