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...

The design of a cellular manufacturing system requires that a part population, at least minimally described by its use of process technology (part/machine incidence matrix), be partitioned into part families and that the associated plant equipment be partitioned into machine cells. At the highest level, the objective is to form a set of completely autonomous units such that inter-cell movement of parts is minimized. We present an integer program that is solved using a genetic algorithm (GA) to assist in the design of cellular manufacturing systems. The formulation uses a unique representation scheme for individuals (part/machine partitions) that reduces the size of the cell formation problem and increases the scale of problems that can be solved. This approach offers improved design flexibility by allowing a variety of evaluation functions to be employed and by incorporating design constraints during cell formation. The effectiveness of the GA approach is demonstrated on several problems from the literature.

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...

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 TD-5FE6RN, October 29, 2002.

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 simultaneous arrangement of part families and machine cells for cellular manufacturing systems. The main feature of the proposed method is, the relevant production data such as process sequences and setup times are taken in to account. It has the ability to select the best solution among the solutions of compactness, group technology efficiency and reducing setup time efficiency for each part before attempting to cluster the machines and parts. The formation of part family and machine cell has been treated as a maximization problem according to a defined performance measure ‘β’. A genetic algorithm has been developed for solving the cell formation problem considering the reduction in setup time. The validation has been done based on a real time manufacturing data. This algorithm is written in the ‘C’ language on Intel Pentium / PIII compatible system.