Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Structural Stereopsis for 3-D Vision
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Stereo Correspondence Through Feature Grouping and Maximal Cliques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
Adaptation in natural and artificial systems
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3-D Shape Recovery Using Distributed Aspect Matching
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An introduction to genetic algorithms
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A Bayesian compatibility model for graph matching
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Structural Matching by Discrete Relaxation
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Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
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Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator
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Efficient Relational Matching with Local Edit Distance
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A sequential niche technique for multimodal function optimization
Evolutionary Computation
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Evolutionary Computation
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This paper presents a method of matching ambiguous feature sets extracted from images. The method is based on Wilson and Hancock's Bayesian matching framework [1], which is extended to handle the case where the feature measurements are ambiguous. A multimodal evolutionary optimisation framework is proposed, which is capable of simultaneously producing several good alternative solutions. Unlike other multimodal genetic algorithms, the one reported here requires no extra parameters: solution yields are maximised by removing bias in the selection step, while optimisation performance is maintained by a local search step. An experimental study demonstrates the effectiveness of the new approach on synthetic and real data. The framework is in principle applicable to any multimodal optimisation problem where local search performs well.