# A robust stochastic genetic algorithm (StGA) for global numerical optimization

@article{Tu2004ARS, title={A robust stochastic genetic algorithm (StGA) for global numerical optimization}, author={Zhenguo Tu and Yong Lu}, journal={IEEE Transactions on Evolutionary Computation}, year={2004}, volume={8}, pages={456-470} }

Many real-life problems can be formulated as numerical optimization of certain objective functions. However, often an objective function possesses numerous local optima, which could trap an algorithm from moving toward the desired global solution. Evolutionary algorithms (EAs) have emerged to enable global optimization; however, at the present stage, EAs are basically limited to solving small-scale problems due to the constraint of computational efficiency. To improve the search efficiency…

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## References

SHOWING 1-10 OF 31 REFERENCES

Enhanced simulated annealing for globally minimizing functions of many-continuous variables

- Computer ScienceTOMS
- 1997

A new global optimization algorithm for functions of many continuous variables is presented, derived from the basic Simulated annealing method, and used to solve complex circuit design problems, for which the objective function evaluation can be exceedingly costly.

Evolutionary programming made faster

- Computer ScienceIEEE Trans. Evol. Comput.
- 1999

A "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator and is proposed and tested empirically, showing that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.

Binary and floating-point function optimization using messy genetic algorithms

- Computer Science
- 1991

This dissertation examines the working of a messy GA, analyzes its operators, extends its use to solve problems of nonuniform building block size and scale, and applies messy GAs to solve a real-world engineering problem that is difficult to solve using a simple GA.

Solving large parameter optimization problems using genetic algorithms

- Computer Science, Mathematics
- 1995

The GA variations presented essentially keep the string lengths as small as possible while maintaining good sampling of the search space to facilitate lower number of function evaluations for finding an optimal solution.

A COMBINED GENETIC AND EIGENSENSITIVITY ALGORITHM FOR THE LOCATION OF DAMAGE IN STRUCTURES

- Computer Science
- 1998

Combining mutation operators in evolutionary programming

- Computer ScienceIEEE Trans. Evol. Comput.
- 1998

Simulations indicate that both the adaptive and nonadaptive versions of this operator are capable of producing solutions that are statistically as good as, or better, than those produced when using Gaussian or Cauchy mutations alone.

Dynamic Parameter Encoding for Genetic Algorithms

- Computer ScienceMachine Learning
- 2004

Dynamic Parameter Encoding is shown to be empirically effective and amenable to analysis; the problem of premature convergence in GAs is explored through two convergence models.

Genetic algorithms + data structures = evolution programs (3rd ed.)

- Computer Science
- 1996

Genetic algorithms are a probabilistic search approach which are founded on the ideas of evolutionary processes and applicable to many hard optimization problems such as optimization of functions with linear and nonlinear constraints.

Genetic Algorithm Approach for Optimal Control Problems with Linearly Appearing Controls

- Mathematics
- 1995

For optimal control problems in Mayer form with all controls appearing only linearly in the equations of motion, this paper presents a method for calculating the optimal solution without…