Soft Computing has been described as computational systems that exploit tolerance for imprecision, uncertainty, partial truth and approximation [8]. Such systems include artificial neural networks, fuzzy systems, evolutionary algorithms and probabilistic reasoning. Artificial Immune Systems (AIS) have recently been proposed as an additional soft computing paradigm [5]. It has been argued that A...

s GO annotations Exession Me thy lat ion Keller/Popescu Tutorial 20 Sequence Comparison: Definitions • Sequence alignment: – A one-to-one matching of two sequences so that each character in a pair of sequences is associated with a single character of the other sequence or with a null character (gap) • Types of alignment: – Pair-wise vs. multiple – Global vs. local – Gapped vs ungapped • Homolog...

Soft Computing models play an important role in the field of recognition, classification, data prediction, etc in various application fields. Soft Computing models include fuzzy logic, neural, network, genetic algorithm, particle swarm optimization, Bacterial forging algotithm, classification and clustering, etc., the extraction of hidden information from large database is possible through the ...

Soft computing is a consortium of methodologies, (like fuzzy logic, neural networks, genetic algorithms, rough sets), that works synergistically and provides , in one form or another, flexible information processing capabilities for handling real life problems. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractabil...

Molecular computing (MC) utilizes the complex interaction of biomolecules and molecular biology protocols to e ect computation. Lab experiments in MC are unreliable, ine cient, unscalable, and expensive compared to conventional computing standards. A critical issue in MC is therefore to test protocols to minimize errors and mishaps that can thwart experiments when actually run in vitro. The pur...

This thesis discusses visual programming languages, representation of uncertainty in geographical data and a combination of genetic programming and optimization. A new visual programming language is described, based on a novel version of the dataflow paradigm. In this version, cyclic graphs are replaced with nested graphs, which also have other uses. Furthermore, the programs become more struct...

This paper reviews the application of principal soft computing techniques for the study of textile processes and products. Soft computing suggests a new computing methodology that is both flexible and easy. Three major branches of soft computing, namely fuzzy logic, neural networks, and genetic algorithms, are discussed in detail with respect to their applications in solving variety of textile ...

This volume is about knowledge processing with interval and soft computing, i.e., about techniques that use both interval and soft computing to process knowledge and about the results of applying these techniques. To better understand these techniques, in Chapter 1, we described fundamentals of interval computing, and in Chapter 2, we described the fundamentals of soft computing. Now it is time...

In this paper we describe how soft computing techniques use in the problem solving approach as we did it as early in a hard computing or traditional define rule base approach. Soft Computing techniques are fuzzy logic and genetic algorithms, Artif icial Neural Networks and Expert System. Soft computing techniques have mainly two important advantages. Firstly, to solve the non-linear problems an...