A self-organizing migrating genetic algorithm for constrained optimization

@article{Deep2008ASM,
  title={A self-organizing migrating genetic algorithm for constrained optimization},
  author={Kusum Deep and Dipti},
  journal={Applied Mathematics and Computation},
  year={2008},
  volume={198},
  pages={237-250}
}
In this paper, a self-organizing migrating genetic algorithm for constrained optimization, called C-SOMGA is presented. This algorithm is based on the features of genetic algorithm (GA) and self-organizing migrating algorithm (SOMA). The aim of this work is to use a penalty free constraint handling selection with our earlier developed algorithm SOMGA (self-organizing migrating genetic algorithm) for unconstrained optimization. C-SOMGA is not only easy to implement but can also provide feasible… CONTINUE READING
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The tunneling algorithm for global optimization of functions

A. V. Levy, A. Montalvo
SIAM Journal of Scientific and Statistical Computing 6 • 1985
View 2 Excerpts
Highly Influenced

Applied nonlinear programming

View 2 Excerpts
Highly Influenced

Integer Programming

H. M. Salkin
Edison Wesley Publishing Com., Amsterdam, 1975. 250 K. Deep, Dipti / Applied Mathematics and Computation 198 • 2008
View 2 Excerpts

Genetic Algorithms for Global Optimization and Their Applications

M. Pant
Ph.D. Thesis, Department of Mathematics, IIT Roorkee, Formerly University of Roorkee, India • 2003
View 2 Excerpts

A Niched-Penalty Approach for Constraint Handling in Genetic Algorithms

KalyanmoyDebandSamirAgrawal KanpurGeneticAlgorithmsLaboratory, Departmentof MechanicalEngineering, IndianInstituteof TechnologyKanpur
2002
View 1 Excerpt

Evolutionary Algorithms for Single and Multicriteria Design Optimization

A. Osyczka
Physica-Verlag Heidelberg, New york • 2002
View 1 Excerpt

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