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

@article{Tu2008CorrectionsT,
  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},
  year={2008},
  volume={12},
  pages={781-781}
}
  • 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. 
Accelerating convergence in cartesian genetic programming by using a new genetic operator
TLDR
A new genetic operator called forking is introduced to accelerate the rate of convergence to interpret individuals dynamically as centers of local Gaussian distributions and allow a sampling process in these distributions when populations get too homogeneous.
Use Particle Swarm Optimization to Optimize Data Search
TLDR
A modified version of PSO is made by using some of the features of the general PSO, which can be used across a wide range of applications, as well as for specific applications focused on a specific requirement.
Intelligent hybrid system for pattern recognition and classification
In this study, we critically analyse and compare performances of several global optimization (GO) approaches with our hybrid GLPτS method, which uses meta-heuristic rules and a local search in the
Quantum Evolutionary Methods for Real Value Problems
TLDR
It is found that the investigated rQIEAs tend to stray from the original quantum computing interpretation, and secondly, their performance on a number of test problems was not as good as claimed in the original publications.
Quantum inspired evolutionary algorithms with improved rotation gates for real-coded synthetic and real world optimization problems
TLDR
Two modified Quantum Evolutionary methods for solving real value problems by introducing and implementing new rotation gate operators used for evolution, including a novel mechanism for preventing premature convergence in the binary algorithm.
Size character optimization for measurement system with binocular vision and optical elements based on local particle swarm method
TLDR
The size character, which represents the relationship of the size variables of a binocular vision model, is studied to determine the optimal structure of the measurement system and the optimal solution with the constraint of the virtual baseline distance is obtained from the particle swarm optimization (PSO) algorithm.
The classification prognosis models of hepatitis b virus reactivation based on Bayes and support vector machine after feature extraction of genetic algorithm
TLDR
Genetic Algorithm (GA) is proposed to extract the key feature subsets of HBV reactivation from the initial feature sets of primary liver carcinoma, and experimental results show that feature extraction based on GA improve the classification performance of HBv reactivation.

References

SHOWING 1-2 OF 2 REFERENCES
A robust stochastic genetic algorithm (StGA) for global numerical optimization
  • Z. Tu, Yong Lu
  • Computer Science
    IEEE Transactions on Evolutionary Computation
  • 2004
TLDR
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
TLDR
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.