Matrix analysis
Combinatorica
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Latent semantic indexing: a probabilistic analysis
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
A similarity-based probability model for latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Spectral techniques for graph bisection in genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic approaches for graph partitioning: a survey
Proceedings of the 13th annual conference on Genetic and evolutionary computation
The inner structure of sensitivities in nodal based shape optimisation
Computational Mechanics
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Several ways of using singular value decomposition (SVD), a linear algebra technique typically used for information retrieval, to decompose problems into subproblems are investigated in the genetic algorithm setting. Empirical evidence, concerning document comparison, indicates that using SVD results both in a savings in storage space and an improvement in information retrieval. Combining theoretical results and algorithms discovered by others, several problems are identified that the SVD can be used with to determine a substructure. Subproblems are discovered by projecting vectors representing the genes of highly fit individuals into a new low-dimensional space, obtained by truncating the SVD of a strategically chosen gene x individual matrix. Techniques are proposed and evaluated that use the subproblems identified by SVD to influence the evolution of the genetic algorithm. By restricting the locus of optimization to the substructure of highly fit individuals, the performance of the genetic algorithm was improved. Performance was also improved by using SVD to genetically engineer individuals out of the subproblems.