This research work examines and verifies the use of subchromosome representations previously introduced to the linkage learning genetic algorithm (LLGA). The subchromosome representation is utilized for effectively lowering the number of building blocks in order to escape from the performance limit implied by the convergence time model for the linkage learning genetic algorithm. A preliminary i...

In this paper we examine the relationship between genetic algorithms (GAs) and traditional methods of experimental design. This was motivated by an investigation into the problem caused by epistasis in the implementation and application of GAs to optimization problems: one which has long been acknowledged to have an important innuence on GA performance. Davidor 1, 2] has attempted an investigat...

Genetic algorithms (GA) have been found to provide global near optimal solutions in a wide range of complex problems. In this paper genetic algorithms have been used to deal with the complex problem of zone design. The zone design problem comprises a large number of geographical tasks, from which electoral districting is probably the most well known. The electoral districting problem is describ...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...

In this paper, we consider Genetic Algorithms for two problems connected with Scan Path Design for VLSI circuits. The first problem, called Partial Scan Path Selection, is to optimize the number of flip-flops to be included in the scan path, at the same time maximizing the fault coverage. The second problem, called Multiple Scan Configuration, is to divide the scan path into a specified number ...

Structural topology optimization is addressed through Genetic Algorithms: A set of designs is evolved following the Darwinian survival-of-ttest principle. The goal is to optimize the weight of the structure under displacement constraints. This approach demonstrates high exibility, and breaks many limits of standard optimization algorithms, in spite of the heavy requirements in term of computati...

This work describes the use of genetic algorithms for automating the photogrammetric network design process. When planning a photogrammetric network, the cameras should be placed in order to satisfjr a set of interrelated and competing constraints. Furthermore, when the object is threedimensional, a combinatorial problem occurs. Genetic algorithms are stochastic optimization techniques, which h...

Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch desig...

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The genetic algorithm (GA) has most often been viewed from a biological perspective. The metaphors of natural selection, cross-breeding and mutation have been helpful in providing a framework in which to explain how and why they work. However, most practical applications of GAs are in the context of optimization, where alternative approaches may prove more eeective. In attempting to understand ...