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}
}
View via Publisher
pse.ice.bas.bg
165 Citations
Highly Influential Citations
4
Background Citations
30
Methods Citations
22

Figures and Tables from this paper

165 Citations

Citation Type

Citation Type

Genetic Algorithm for Constrained Optimization with Stepwise Approach in Search Interval Selection of Variables
TLDR
Based on the result of the present work it can be concluded that the optimal values obtained for all the three test problems evaluated using the stepwise approach are better than those obtained using GA withoutStepwise approach & conventional techniques.
Modified genetic algorithm with sampling techniques for chemical engineering optimization
Bull optimization algorithm based on genetic operators for continuous optimization problems
TLDR
The researcher proposes a new evolutionary optimization algorithm that depends on genetic operators such as crossover and mutation, referred to as the bull optimization algorithm (BOA), which provided better results than the optimization algorithms that are most commonly used in solving continuous optimization problems.
Properties of a genetic algorithm equipped with a dynamic penalty function
Multi-Objective Genetic Algorithms for Chemical Engineering Applications
TLDR
Two multi-objective genetic algorithms to tackle continuous and mixed-integer chemical engineering problems and the results are compared with the ones reported in the literature and the analysis highlights the efficiency of the proposed algorithms.
Optimization Strategy Based on Genetic Algorithms and Neural Networks Applied to a Polymerization Process
The article presents a simple and general methodology, especially destined to the optimization of complex, strongly nonlinear systems, for which no extensive knowledge or precise models are…
Bat algorithm: a novel approach for global engineering optimization
TLDR
A new nature‐inspired metaheuristic optimization algorithm, called bat algorithm (BA), based on the echolocation behavior of bats is introduced, and the optimal solutions obtained are better than the best solutions obtained by the existing methods.
Solving nonlinear constrained optimization problems using hybrid evolutionary algorithms
TLDR
Comparison established against other algorithms proves that the proposed algorithm preserve finding the optimal solution while reduces the function evaluations.
Optimal Solution of MINLP Problems Using Modified Genetic Algorithm
TLDR
Six MINLP problems, which emerged from the optimal design of sequential multi-product batch plants, and considered as difficult ones in literature, were successfully solved and the solutions thus obtained are either comparable or better than those available in literature.
Confrontation of Genetic Algorithm Optimization Process with a New Reference Case: Analytical Study with Experimental Validation of the Deflection of a Cantilever Beam
This paper deals with the optimization of a cantilever beam submitted to its own weight. In a first approach, we consider that the beam section can be equal to two values and we are looking for the…
...
...

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.
Solution of constrained optimization problems by multi-objective genetic algorithm
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
...
...