Corpus ID: 118413787

Evaluation-Relaxation Schemes for Genetic and Evolutionary Algorithms

@inproceedings{Sastry2004EvaluationRelaxationSF,
  title={Evaluation-Relaxation Schemes for Genetic and Evolutionary Algorithms},
  author={K. Sastry and M. Pelikan and Prasanna Parthasarathy and R. Srivastava and A. Sinha and Franz Rothlauf},
  year={2004}
}
  • K. Sastry, M. Pelikan, +3 authors Franz Rothlauf
  • Published 2004
  • Computer Science
  • Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by the earlier GAs to be solvable, requiring only a subquadratic number of function evaluations. To facilitate solving large-scale complex problems, and to further enhance the performance of competent GAs… CONTINUE READING
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    References

    SHOWING 1-10 OF 222 REFERENCES
    A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
    • 1,338
    • PDF
    Designing Efficient Genetic and Evolutionary Algorithm Hybrids
    • 32
    Distributed genetic algorithms for function optimization
    • 142
    Fitness inheritance in genetic algorithms
    • 189
    • PDF
    On Evolutionary Optimization with Approximate Fitness Functions
    • 158
    • Highly Influential
    • PDF
    Genetic Algorithms in Noisy Environments
    • 163
    • PDF
    Optimal Sampling For Genetic Algorithms
    • 45
    • Highly Influential
    Messy Genetic Algorithms: Motivation, Analysis, and First Results
    • 1,331
    Replacement Strategies in Steady State Genetic Algorithms: Static Environments
    • 42
    • PDF