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 Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
ASPARAGOS, A Parallel Genetic Algorithm and Population Genetics
WOPPLOT '89 Workshop on Evolutionary Models and Strategies, Workshop on Parallel Processing: Logic, Organization, and Technology: Parallelism, Learning, Evolution
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
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
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This paper considers how ambiguous graph matching can be realised using a hybrid genetic algorithm. The problem we address is how to maximise the solution yield of the genetic algorithm when the available attributes are ambiguous. We focus on the role of the selection operator. A multi-modal evolutionary optimisation framework is proposed, which is capable of simultaneously producing several good alternative solutions. Unlike other multi-modal 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.