An Adaptive Penalty Scheme In Genetic Algorithms For Constrained Optimiazation Problems

@inproceedings{Barbosa2002AnAP,
  title={An Adaptive Penalty Scheme In Genetic Algorithms For Constrained Optimiazation Problems},
  author={Helio J. C. Barbosa and Afonso C. C. Lemonge},
  booktitle={GECCO},
  year={2002}
}
Repair methods use domain knowledge in order to move infeasible offspring into the feasible set. However there are situations when it is very expensive, or even impossible, to construct such a repair operator, drastically reducing the range of applicability of repair methods. Like repair methods, the design of special decoders[1] that always extract a feasible phenotype from a given genotype is not trivial in general and cannot always be done. In special situations genetic operators can be… CONTINUE READING
Highly Cited
This paper has 52 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 28 extracted citations

Progress in Artificial Intelligence

Lecture Notes in Computer Science • 2015
View 10 Excerpts
Highly Influenced

Bat algorithm for constrained optimization tasks

Neural Computing and Applications • 2012
View 3 Excerpts
Highly Influenced

Constrained Nonnegative Matrix Factorization Based on Particle Swarm Optimization for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing • 2017

53 Citations

0510'03'06'10'14'18
Citations per Year
Semantic Scholar estimates that this publication has 53 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 27 references

Genetic Algorithms + Data Structures = Evolution Programs

Artificial Intelligence • 1992
View 6 Excerpts
Highly Influenced

Stochastic ranking for constrained evolutionary optimization

IEEE Trans. Evolutionary Computation • 2000
View 4 Excerpts
Highly Influenced

Genetic algorithms: A fitness formulation for constrained minimization

J. A. Wright, R. Farmani
Proc. of the Genetic and Evolutionary Computation Conference – GECCO 2001, pages 725–732, San Francisco, CA., • 2001
View 2 Excerpts

How to Solve It: Modern Heuristics

Springer Berlin Heidelberg • 2000
View 2 Excerpts

The need for improving the exploration operators for constrained optimization problems

S. B. Hamida, A. Petrowski
2000 Congress on Evolutionary Computation, pages 1176–1183, San Diego, CA, USA, July • 2000
View 1 Excerpt

A coevolutionary genetic algorithm for constrained optimization problems

H.J.C. Barbosa
Proc. of the Congress on Evolutionary Computation, pages 1605– 1611, Washington, DC, USA, • 1999
View 1 Excerpt

Similar Papers

Loading similar papers…