Corrections to “A Robust Stochastic Genetic Algorithm (StGA) for Global Numerical Optimization”

  title={Corrections to “A Robust Stochastic Genetic Algorithm (StGA) for Global Numerical Optimization”},
  author={Zhenguo Tu and Yong Lu},
  journal={IEEE Transactions on Evolutionary Computation},
  • Z. Tu, Yong Lu
  • Published 1 December 2008
  • Computer Science
  • IEEE Transactions on Evolutionary Computation
In the above titled paper (ibid., vol 8, no. 5, pp. 456-470, Oct 04), there is an error in the programming of the stochastic genetic algorithm (StGA) presented in that paper. The origin and implications of the error are discussed here. 
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A robust stochastic genetic algorithm (StGA) for global numerical optimization
  • Z. Tu, Yong Lu
  • Computer Science
    IEEE Transactions on Evolutionary Computation
  • 2004
Compared with several other algorithms, the StGA achieves not only an improved accuracy, but also a considerable reduction of the computational effort; on average, the computational cost required by StGA is about one order less than the other algorithms.
Dominance-Based Multiobjective Simulated Annealing
A multiobjective simulated annealer utilizing the relative dominance of a solution as the system energy for optimization, eliminating problems associated with composite objective functions is proposed and a method for choosing perturbation scalings promoting search both towards and across the Pareto front is proposed.