BASIC - A genetic algorithm for engineering problems solution

@article{Shopova2006BASICA,
  title={BASIC - A genetic algorithm for engineering problems solution},
  author={Elisaveta G. Shopova and Natasha G. Vaklieva-Bancheva},
  journal={Comput. Chem. Eng.},
  year={2006},
  volume={30},
  pages={1293-1309}
}

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References

SHOWING 1-10 OF 50 REFERENCES
Augmented Lagrangian genetic algorithm for structural optimization
TLDR
This paper presents a robust hybrid genetic algorithm for optimization of space structures using the augmented Lagrangian method that can be applied to a broad class of optimization problems.
Constraint consistent genetic algorithms
  • R. Kowalczyk
  • Computer Science
    Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)
  • 1997
TLDR
Initial experiments indicate that in the terms of the solution quality and the number of iterations the constraint consistency based approach in CCGA can outperform other constraint handling methods in GA for a number of selected test problems.
An Efficient Constraint Handling Method for Genetic Algorithms
Handbook Of Genetic Algorithms
TLDR
This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
TLDR
A rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs) and the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM.
Evolutionary Algorithms in Engineering Applications
TLDR
This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering and can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.
Parametric Sensitivity and Search-Space Characterization Studies of Genetic Algorithms for Computer-Aided Polymer Design
TLDR
The results of this study indicate that the performance of the GA could be enhancers on a large scale polymer design problem, and a parametric sensitivity study for GA-based polymer design is described.
Genetic algorithms + data structures = evolution programs (3rd ed.)
TLDR
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 Programming
...
...