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Empirical Investigation of the Benefits of Partial Lamarckianism
, 1997
"... Genetic algorithms (GAs) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, which are good at ..."
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Cited by 18 (2 self)
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Genetic algorithms (GAs) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, which are good at finding local optima, and genetic algorithms. There are two basic strategies for using hybrid GAs. In the first, Lamarckian learning, the genetic representation is updated to match the solution found by the improvement procedure. In the second, Baldwinian learning, improvement procedures are used to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper examines the issue of using partial Lamarckianism, i.e., the updating of the genetic representation for only a percentage of the individuals, as compared to pure Lamarckian and pure Baldwinian learning in hybrid GAs. Multiple instances of five bounded nonlinear problems, the locat...
Cellular Manufacturing System Design Using a Holonistic Approach
, 2000
"... We propose an integrated algorithm that will solve... ..."
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Cited by 5 (0 self)
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We propose an integrated algorithm that will solve...
Utilizing Lamarckian Evolution and the Baldwin Effect in Hybrid Genetic Algorithms
, 1996
"... Genetic algorithms(GA) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, usually working as ..."
Abstract

Cited by 3 (1 self)
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Genetic algorithms(GA) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, usually working as evaluation functions, and genetic algorithms. There are two basic strategies in using hybrid GAs, Lamarckian and Baldwinian learning. Traditional schema theory does not support Lamarckian learning, i.e., forcing the genetic representation to match the solution found by the improvement procedure. However, Lamarckian learning does alleviate the problem of multiple genotypes mapping to the same phenotype. Baldwinian learning uses improvement procedures to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper empirically examines the issues of using Lamarckian and Baldwinian learning in hybrid GAs. In the empirical investigation cond...
R.: An Indexed Model
 of Impredicative Polymorphism and Mutable References, January 2003, Available at http://www.cs.princeton.edu/ ∼ appel/papers/impred.pdf
"... A comparison of heuristic methods for ..."
A Hybrid Genetic Algorithm for Manufacturing Cell Formation
"... Cellular manufacturing emerged as a production strategy capable of solving the problems of complexity and long manufacturing lead times in batch production. The fundamental problem in cellular manufacturing is the formation of product families and machine cells. This paper presents a new approach fo ..."
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Cellular manufacturing emerged as a production strategy capable of solving the problems of complexity and long manufacturing lead times in batch production. The fundamental problem in cellular manufacturing is the formation of product families and machine cells. This paper presents a new approach for obtaining machine cells and product families. The approach combines a local search heuristic with a genetic algorithm. Computational experience with the algorithm on a set of group technology problems available in the literature is also presented. The approach produced solutions with a grouping efficacy that is at least as good as any results previously reported in literature and improved the grouping efficacy for 59 % of the problems. Keywords: Cellular Manufacturing; Group Technology; Genetic Algorithms; Random Keys AT&T Labs Research Technical Report TD5FE6RN, October 29, 2002.
Int. J. Prod. Res., 2000, Vol. 38, No. 5, 1201 1218
 International Journal of Production Research
, 2000
"... this paper is to provide a review and comparison of the approaches to multicriteria decisionmaking (MCDM) in the design of manu  facturing cells. A brief description of existing classi cations is provided, together with an overview on the MCDM. Selected papers are reviewed and a structured schem ..."
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this paper is to provide a review and comparison of the approaches to multicriteria decisionmaking (MCDM) in the design of manu  facturing cells. A brief description of existing classi cations is provided, together with an overview on the MCDM. Selected papers are reviewed and a structured scheme is outlined which allows comparison of inputs, criteria, solution approaches and outputs across selected models. Finally the models are discussed and directions for new avenues of work are identi ed
Proceedings of the 2002 Winter Simulation Conference
"... A simulation model is successful if it leads to policy action, i.e., if it is implemented. Studies show that for a model to be implemented, it must have good correspondence with the mental model of the system held by the user of the model. The user must feel confident that the simulation model corre ..."
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A simulation model is successful if it leads to policy action, i.e., if it is implemented. Studies show that for a model to be implemented, it must have good correspondence with the mental model of the system held by the user of the model. The user must feel confident that the simulation model corresponds to this mental model. An understanding of how the model works is required. Simulation models for implementation must be developed step by step, starting with a simple model, the simulation prototype. After this has been explained to the user, a more detailed model can be developed on the basis of feedback from the user. Software for simulation prototyping is discussed, e.g., with regard to the ease with which models and output can be explained and the speed with which small models can be written.
www.elsevier.com/locate/dsw An evolutionary algorithm for manufacturing cell formation *
, 2003
"... Cellular manufacturing emerged as a production strategy capable of solving the certain problems of complexity and long manufacturing lead times in batch production. The fundamental problem in cellular manufacturing is the formation of product families and machine cells. This paper presents a new app ..."
Abstract
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Cellular manufacturing emerged as a production strategy capable of solving the certain problems of complexity and long manufacturing lead times in batch production. The fundamental problem in cellular manufacturing is the formation of product families and machine cells. This paper presents a new approach for obtaining machine cells and product families. The approach combines a local search heuristic with a genetic algorithm. Computational experience with the algorithm on a set of group technology problems available in the literature is also presented. The approach produced solutions with a grouping efficacy that is at least as good as any results previously reported in literature and improved the grouping efficacy for 59 % of the problems. q 2004 Published by Elsevier Ltd.
A HEURISTIC METHOD FOR THE PARTMACHINE GROUPING PROBLEM
"... Constructing a cell is a critical and important element of the cellular manufacturing problem. Generally, part routes are a helpful tool for this constructing process. Not to consider the alternative routes mean to undervalue the better cells. On the other hand, considering them may increase the com ..."
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Constructing a cell is a critical and important element of the cellular manufacturing problem. Generally, part routes are a helpful tool for this constructing process. Not to consider the alternative routes mean to undervalue the better cells. On the other hand, considering them may increase the complexity of the problem. In this study, in the existence of the alternative routes, a genetic algorithms methodology is proposed. This methodology, also, determines the best cell number. The performance of the algorithm is tested via the computational experiments.. The results indicate the success of the algorithm for not only the cell constructing problems considering the alternative routes but also the general cell constructing problems.