Dependency Structure Matrix, Genetic Algorithms, and Effective Recombination

@article{Yu2009DependencySM,
  title={Dependency Structure Matrix, Genetic Algorithms, and Effective Recombination},
  author={Tian-Li Yu and D. Goldberg and K. Sastry and C. Lima and M. Pelikan},
  journal={Evolutionary Computation},
  year={2009},
  volume={17},
  pages={595-626}
}
  • Tian-Li Yu, D. Goldberg, +2 authors M. Pelikan
  • Published 2009
  • Mathematics, Medicine, Computer Science
  • Evolutionary Computation
    • In many different fields, researchers are often confronted by problems arising from complex systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition is the key. In this paper, investigating and analyzing interactions between components of complex systems shed some light on problem decomposition. By recognizing three bare-bones interactionsmodularity, hierarchy, and overlap, facet… CONTINUE READING
    View on MIT Press
    sapientia.ualg.pt
    75 Citations
    Highly Influential Citations
    9
    Background Citations
    30
    Methods Citations
    25
    75 Citations
    Citation Type

    Citation Type

    Efficient model building in competent genetic algorithms using DSM clustering
    • 5
    Maximum spanning tree based linkage learner
    • 2
    A Metaheuristic Relying on Random Walk on a Graph for Binary Optimization Problems
    • T. Sato, K. Ohnishi
    • Computer Science
    • 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
    • 2018
    • Highly Influenced
    Linkage learning using the maximum spanning tree of the dependency graph
    • 2
    • PDF
    Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization
    • 393
    • PDF
    Interaction-detection metric with differential mutual complement for dependency structure matrix genetic algorithm
    • 6
    • PDF
    Exploiting Bivariate Dependencies to Speedup Structure Learning in Bayesian Optimization Algorithm
    • 2
    An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings
    Decomposition-based evolutionary algorithm for large scale constrained problems
    • 17
    • PDF
    Linkage learning by number of function evaluations estimation: Practical view of building blocks
    • 4

    References

    SHOWING 1-10 OF 83 REFERENCES
    Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
    • 223
    • PDF
    An information theoretic method for developing modular architectures using genetic algorithms
    • 166
    • PDF
    Schemata, Distributions and Graphical Models in Evolutionary Optimization
    • 359
    Model accuracy in the Bayesian optimization algorithm
    • 30
    • PDF
    Overcoming hierarchical difficulty by hill-climbing the building block structure
    • 28
    • PDF
    Messy Genetic Algorithms: Motivation, Analysis, and First Results
    • 1,332
    Compact Genetic Codes as a Search Strategy of Evolutionary Processes
    • 20
    • PDF
    Linkage learning, overlapping building blocks, and systematic strategy for scalable recombination
    • 34
    • PDF
    An Introduction to Genetic Algorithms.
    • 7,198
    • PDF
    A crossover for complex building blocks overlapping
    • 16
    • PDF