A Genetic Algorithm Approach to the Buyers' Welfare Problem of Product Line Design: A Comparative Computational Study

Georgia Alexouda; Konstantinos Paparrizos

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Displaying similar documents to “A Genetic Algorithm Approach to the Buyers' Welfare Problem of Product Line Design: A Comparative Computational Study”

Solving the simple plant location problem by genetic algorithm

Jozef Kratica, Dušan Tošic, Vladimir Filipović, Ivana Ljubić (2001)

RAIRO - Operations Research - Recherche Opérationnelle

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The simple plant location problem (SPLP) is considered and a genetic algorithm is proposed to solve this problem. By using the developed algorithm it is possible to solve SPLP with more than 1000 facility sites and customers. Computational results are presented and compared to dual based algorithms.

Mapping the convergence of genetic algorithms.

Drezner, Zvi, Marcoulides, George A. (2006)

Journal of Applied Mathematics and Decision Sciences

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A New Genetic Representation for Quadratic Assignment Problem

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Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms

Angelova, Maria, Tzonkov, Stoyan, Pencheva, Tania (2010)

Serdica Journal of Computing

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Fermentation processes as objects of modelling and high-quality control are characterized with interdependence and time-varying of process variables that lead to non-linear models with a very complex structure. This is why the conventional optimization methods cannot lead to a satisfied solution. As an alternative, genetic algorithms, like the stochastic global optimization method, can be applied to overcome these limitations. The application of genetic algorithms is a precondition for...

A new evolutionary algorithm for the multiobjective $0 / 1$ knapasck problem.

Groşan, Crina, Oltean, Mihai, Dumitrescu, D. (2003)

Acta Universitatis Apulensis. Mathematics - Informatics

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The grouping differential evolution algorithm for multi-dimensional optimization problems

Adam Piotrowski, Jarosław Napiórkowski (2010)

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Experiments with variants of ant algorithms.

Thomas Stützle, Sebastian Linke (2002)

Mathware and Soft Computing

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A number of extensions of Ant System, the first ant colony optimization (ACO) algorithm, were proposed in the literature. These extensions typically achieve much improved computational results when compared to the original Ant System. However, many design choices of Ant System are left untouched including the fact that solutions are constructed, that real-numbers are used to simulate pheromone trails, and that explicit pheromone evaporation is used. In this article we experimentally...

Bipolar convergence in genetic algorithm for multimodal optimization.

Rotar, Corina (2003)

Acta Universitatis Apulensis. Mathematics - Informatics

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MEMOTS: a memetic algorithm integrating tabu search for combinatorial multiobjective optimization

Thibaut Lust, Jacques Teghem (2008)

RAIRO - Operations Research

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We present in this paper a new multiobjective memetic algorithm scheme called MEMOX. In current multiobjective memetic algorithms, the parents used for recombination are randomly selected. We improve this approach by using a dynamic hypergrid which allows to select a parent located in a region of minimal density. The second parent selected is a solution close, in the objective space, to the first parent. A local search is then applied to the offspring. We experiment this scheme with...

Advances in parallel heterogeneous genetic algorithms for continuous optimization

Enrique Alba, Francisco Luna, Antonio Nebro (2004)

International Journal of Applied Mathematics and Computer Science

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In this paper we address an extension of a very efficient genetic algorithm (GA) known as Hy3, a physical parallelization of the gradual distributed real-coded GA (GD-RCGA). This search model relies on a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimization by using crossover operators tuned to explore and exploit the solutions...