An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Binary and floating-point function optimization using messy genetic algorithms
Binary and floating-point function optimization using messy genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic algorithms for graph partitioning and incremental graph partitioning
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
The Symbiotic Evolution of Solutions and Their Representations
Proceedings of the 6th International Conference on Genetic Algorithms
Metabits: Generic Endogenous Crossover Control
Proceedings of the 6th International Conference on Genetic Algorithms
Techniques for reducing the disruption of superior building blocks in genetic algorithms
Techniques for reducing the disruption of superior building blocks in genetic algorithms
Linkage crossover operator for genetic algorithms
Linkage crossover operator for genetic algorithms
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A variable-grouping based genetic algorithm for large-scale integer programming
Information Sciences: an International Journal
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Problem-specific knowledge is often implemented in search algorithms using heuristics to determine which search paths are to be explored at any given instant. As in other search methods, utilizing this knowledge will more quickly lead a genetic algorithm (GA) towards better results. In many problems, crucial knowledge is not found in individual components, but in the interrelations between those components. For such problems, we develop an interrelation (linkage) based crossover operator that has the advantage of liberating GAs from the constraints imposed by the fixed representations generally chosen for problems. The strength of linkages between components of a chromosomal structure can be explicitly represented in a linkage matrix and used in the reproduction step to generate new individuals. For some problems, such a linkage matrix is known a priori from the nature of the problem. In other cases, the linkage matrix may be learned by successive minor adaptations during the execution of the evolutionary algorithm. This paper demonstrates the success of such an approach for several problems.