Association Rules are the most important tool to discover the relationships among the attributes in a database. The existing Association Rule mining algorithms are applied on binary attributes or discrete attributes, in case of discrete attributes there is a loss of information and these algorithms take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA)...

One of the advantages of functional languages is the ability to use higher-order functions to guide the design of certain classes of algorithms. This paper is concerned with genetic algorithms (GAs) and their application to the solution of the single row routing (SRR) problem which is an important problem in the design of multilayer printed circuit boards (MPCBs). The paper presents a framework...

The design of efficient monitoring networks is very important to obtain a better understanding of environmental, ecological and epidemiological processes. In this paper we develop for the optimal design of monitoring networks a new hybrid genetic algorithm (HGA) which combines the standard genetic algorithm (GA) with a local search operator. We compare the performance of our HGA with two other ...

Linkage identification algorithms identify linkage groups — sets of loci tightly linked — before genetic optimizations for their recombination operators to work effectively and reliably. This paper proposes a parallel genetic algorithm (GA) based on the linkage identification algorithm and shows its effectiveness compared with other conventional parallel GAs such as master-slave and island mode...

.............................................................................................................................. 5 Introduction......................................................................................................................... 5 Improvements to Genetic Algorithms................................................................................. 6 Precedence Rel...

A hybrid learning automata-genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS)...

In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimizatio...

This paper presents a massively parallel Genetic Algorithm – Pattern Search (GA-PS) with graphics hardware acceleration on bound constrained nonlinear optimization problems. The objective of this study is to determine the effectiveness of using Graphics Processing Units (GPU) as a hardware platform for Genetic Algorithms (GA). The global search of the GA is enhanced by a local Pattern Search (P...

Genetic Algorithms (GA) is well known for searching global maxima and minima[1, 2]. In general, number of search points required by GA for searching global extreme is much lower than the exhausted search. GA has been applied to Block Matching Algorithm (BMA) and demonstrates positively its capability in BMA. The mean square error (MSE) performance of GA based BMA is close to full search (FS). H...

This paper presents a mathematical model for designing cellular manufacturing systems (CMSs) solved by genetic algorithms. This model assumes a dynamic production, a stochastic demand, routing flexibility, and machine flexibility. CMS is an application of group technology (GT) for clustering parts and machines by means of their operational and / or apparent form similarity in different aspects ...