Contents

Cover

Half Title page

Title page

Copyright page

Dedication

Preface

Contributors

Part I: Introduction

Chapter 1: Computational Intelligence: Foundations, Perspectives, and Recent Trends

1.1 What is Computational Intelligence?

1.2 Classical Components of CI

1.3 Hybrid Intelligent Systems in CI

1.4 Emerging Trends in CI

1.5 Summary

References

Chapter 2: Fundamentals of Pattern Analysis: A Brief Overview

2.1 Introduction

2.2 Pattern Analysis: Basic Concepts and Approaches

2.3 Feature Selection

2.4 Pattern Classification

2.5 Unsupervised Classification or Clustering

2.6 Neural Network Classifier

2.7 Conclusion

References

Chapter 3: Biological Informatics: Data, Tools, and Applications

3.1 Introduction

3.2 Data

3.3 Tools

3.4 Applications

3.5 Conclusion

References

Part II: Sequence Analysis

Chapter 4: Promoter Recognition Using Neural Network Approaches

4.1 Introduction

4.2 Related Literature /Background

4.3 Global Signal-Based Methods for Promoter Recognition

4.4 Challenges in Promoter Classification

4.5 Conclusions

4.6 Future directions

References

Chapter 5: Predicting Microrna Prostate Cancer Target Genes

5.1 Introduction

5.2 miRNA and Prostate Cancer

5.3 Prediction software for miRNAs

5.4 miRanda

5.5 Proposed method

5.6 Automatic parameter tuning

5.7 Experimental analysis

5.8 Discussion and Conclusions

Acknowledgments

References

Part III: Structure Analysis

Chapter 6: Structural Search in RNA Motif Databases

6.1 Introduction

6.2 The Search Engine on RmotifDB

6.3 The Search Engine Based on BlockMatch

6.4 Conclusion

Acknowledgments

References

Chapter 7: Kernels on Protein Structures

7.1 Introduction

7.2 Kernels Methods

7.3 Protein Structures

7.4 Kernels on Neighborhoods

7.5 Kernels on Protein Structures

7.6 Experimental Results

7.7 Discussion and Conclusion

Appendix A

References

Chapter 8: Characterization of Conformational Patterns in Active and Inactive Forms of Kinases Using Protein Blocks Approach

8.1 Introduction

8.2 Distinguishing conformational variations from rigid-body shifts in active and inactive forms of a kinase

8.3 Cross comparison of active and inactive forms of closely related kinases

8.4 Comparison of the active states of homologous kinases

8.5 Conclusions

Acknowledgments

References

Chapter 9: Kernel Function Applications in Cheminformatics

9.1 Introduction

9.2 Background

9.3 Related Works

9.4 Alignment Kernels with Pattern-based Features

9.5 Alignment Kernels with Approximate Pattern Features

9.6 Matching Kernels with Approximate Pattern-based Features

9.7 Graph Wavelets for Topology Comparison

9.8 Conclusions

References

Chapter 10: In Silico Drug Design Using a Computational Intelligence Technique

10.1 Introduction

10.2 Proposed Methodology

10.3 Experimental Results and Discussion

10.4 Conclusion

References

Part IV: Microarray Data Analysis

Chapter 11: Integrated Differential Fuzzy Clustering for Analysis of Microarray Data

11.1 Introduction

11.2 Clustering Algorithms and Validity Measure

11.3 Differential Evolution based Fuzzy Clustering

11.4 Experimental Results

11.5 Integrated Fuzzy clustering with Support Vector Machines

11.6 Conclusion

References

Chapter 12: Identifying Potential Gene Markers Using Svm Classifier Ensemble

12.1 Introduction

12.2 Microarray Gene Expression Data

12.3 Support Vector Machine Classifier

12.4 Proposed Technique

12.5 Data Sets and Preprocessing

12.6 Experimental Results

12.7 Discussion and Conclusions

Acknowledgment

References

Chapter 13: Gene Microarray Data Analysis Using Parallel Point Symmetry-Based Clustering

13.1 Introduction

13.2 Symmetry- and point symmetry-based distance measures

13.3 Parpsbkm clustering implementation

13.4 Performance analysis

13.5 Test for Statistical Significance

13.6 Conclusions

References

Part V: Systems Biology

Chapter 14: Techniques For Prioritization of Candidate Disease Genes

14.1 Introduction

14.2 Prioritization Based on Text-Mining With Reference to Phenotypes

14.3 Prioritization with no direct reference to phenotypes

14.4 Prioritization using interaction networks

14.5 Prioritization based on joint use of interaction network and literature-based similarity between phenotypes

14.6 Fusion of data from multiple sources

14.7 Conclusions and open problems

14.8 Acknowledgment

References

Chapter 15: Prediction of Protein–Protein Interactions

15.1 Introduction

15.2 Basic Definitions

15.3 Classification of PPI

15.4 Characteristics of PPIs

15.5 Driving Forces for the Formation of PPIs

15.6 Prediction of PPIs

15.7 Discussion and Conclusion

Appendix I

Appendix II

References

Chapter 16: Analyzing Topological Properties of Protein–Protein Interaction Networks: A Perspective Toward Systems Biology

16.1 Introduction

16.2 Topology of PPI Networks

16.3 Literature Survey

16.4 Problem Discussion

16.5 Theoretical Analysis

16.6 Algorithmic Approach

16.7 Empirical Analysis

16.8 Conclusions

Acknowledgment

References

Index

COMPUTATIONAL INTELLIGENCE AND PATTERN ANALYSIS IN BIOLOGICAL INFORMATICS

Wiley Series on
Bioinformatics: Computational Techniques and Engineering

Bioinformatics and computational biology involve the comprehensive application of mathematics, statistics, science, and computer science to the understanding of living systems. Research and development in these areas require cooperation among specialists from the fields of biology, computer science, mathematics, statistics, physics, and related sciences. The objective of this book series is to provide timely treatments of the different aspects of bioinformatics spanning theory, new and established techniques, technologies and tools, and application domains. This series emphasizes algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology.

Series Editors: Professor Yi Pan and Professor Albert Y. Zomaya

pan@cs.gsu.edu zomaya@it.usyd.edu.au

Knowledge Discovery in Bioinformatics: Techniques, Methods, and Applications

Xiaohua Hu and Yi Pan

Grid Computing for Bioinformatics and Computational Biology

Edited by El-Ghazali Talbi and Albert Y. Zomaya

Bioinformatics Algorithms: Techniques and Applications

Ion Mandiou and Alexander Zelikovsky

Analysis of Biological Networks

Edited by Björn H. Junker and Falk Schreiber

Computational Intelligence and Pattern Analysis in Biological Informatics

Edited by Ujjwal Maulik, Sanghamitra Bandyopadhyay, and Jason T. L. Wang

Copyright © 2010 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

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ISBN 978-0-470-58159-9

Library of Congress Cataloging-in-Publication Data is available.

To Utsav, our students and parents
—U. Maulik and
S. Bandyopadhyay

To my wife Lynn and
daughter Tiffany
—J. T. L. Wang

Preface

Computational biology is an interdisciplinary field devoted to the interpretation and analysis of biological data using computational techniques. It is an area of active research involving biology, computer science, statistics, and mathematics to analyze biological sequence data, genome content and arrangement, and to predict the function and structure of macromolecules. This field is a constantly emerging one, with new techniques and results being reported every day. Advancement of data collection techniques is also throwing up novel challenges for the algorithm designers to analyze the complex and voluminous data. It has already been established that traditional computing methods are limited in their scope for application to such complex, large, multidimensional, and inherently noisy data. Computational intelligence techniques, which combine elements of learning, adaptation, evolution, and logic, are found to be particularly well suited to many of the problems arising in biology as they have flexible information processing capabilities for handling huge volume of real-life data with noise, ambiguity, missing values, and so on. Solving problems in biological informatics often involves search for some useful regularities or patterns in large amounts of data that are typically characterized by high dimensionality and low sample size. This necessitates the development of advanced pattern analysis approaches since the traditional methods often become intractable in such situations.

In this book, we attempt to bring together research articles by active practitioners reporting recent advances in integrating computational intelligence and pattern analysis techniques, either individually or in a hybridized manner, for analyzing biological data in order to extract more and more meaningful information and insights from them. Biological data to be considered for analysis include sequence, structure, and microarray data. These data types are typically complex in nature, and require advanced methods to deal with them. Characteristics of the methods and algorithms reported here include the use of domain-specific knowledge for reducing the search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sublinear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers. The techniques can be sequential or parallel in nature.

Computational Intelligence (CI) is a successor of artificial intelligence that combines elements of learning, adaptation, evolution, and logic to create programs that are, in some sense, intelligent. Computational intelligence exhibits an ability to learn and/or to deal with new situations, such that the system is perceived to possess one or more attributes of reason, (e.g., generalization, discovery, association, and abstraction). The different methodologies in CI work synergistically and provide, in one form or another, flexible information processing capabilities. Many biological data are characterized by high dimensionality and low sample size. This poses grand challenges to the traditional pattern analysis techniques necessitating the development of sophisticated approaches.

This book has five parts. The first part contains chapters introducing the basic principles and methodologies of computational intelligence techniques along with a description of some of its important components, fundamental concepts in pattern analysis, and different issues in biological informatics, including a description of biological data and their sources. Detailed descriptions of the different applications of computational intelligence and pattern analysis techniques to biological informatics constitutes the remaining chapters of the book. These include tasks related to the analysis of sequences in the second part, structures in the third part, and microarray data in part four. Some topics in systems biology form the concluding part of this book.

In Chapter 1, Das {et al.} present a lucid overview of computational intelligence techniques. They introduce the fundamental aspects of the key components of modern computational intelligence. A comprehensive overview of the different tools of computational intelligence (e.g., fuzzy logic, neural network, genetic algorithm, belief network, chaos theory, computational learning theory, and artificial life) is presented. It is well known that the synergistic behavior of the above tools often far exceeds their individual performance. A description of the synergistic behaviors of neuro-fuzzy, neuro-GA, neuro-belief, and fuzzy-belief network models is also included in this chapter. It concludes with a detailed discussion on some emerging trends in computational intelligence like swarm intelligence, Type-2 fuzzy sets, rough sets, granular computing, artificial immune systems, differential evolution, bacterial foraging optimization algorithms, and the algorithms based on artificial bees foraging behavior.

Chakraborty provides an overview of the basic concepts and the fundamental techniques of pattern analysis with an emphasis on statistical methods in Chapter 2. Different approaches for designing a pattern recognition system are described. The pattern recognition tasks of feature selection, classification, and clustering are discussed in detail. The most popular statistical tools are explained. Recent approaches based on the soft computing paradigm are also introduced in this chapter, with a brief representation of the promising neural network classifiers as a new direction toward dealing with imprecise and uncertain patterns generated in newer fields.

In Chapter 3, Byron et al. deal with different aspects of biological informatics. In particular, the biological data types and their sources are mentioned, and two software tools used for analyzing the genomic data are discussed. A case study in biological informatics, focusing on locating noncoding RNAs in Drosophila genomes, is presented. The authors show how the widely used Infernal and RSmatch tools can be combined to mine roX1 genes in 12 species of Drosophila for which the entire genomic sequencing data is available.

The second part of the book, Chapters 4 and 5, deals with the applications of computational intelligence and pattern analysis techniques for biological sequence analysis. In Chapter 4, Rani et al. extract features from the genomic sequences in order to predict promoter regions. Their work is based on global signal-based methods using a neural network classifier. For this purpose, they consider two global features: n-gram features and features based on signal processing techniques by mapping the sequence into a signal. It is shown that the n-gram features extracted for n=2,3,4, and 5 efficiently discriminate promoters from nonpromoters.

In Chapter 5, Masulli et al. deal with the task of computational prediction of microRNA (miRNA) targets with focus on miRNAs’ influence in prostate cancer. The miRNAs are capable of base-pairing with imperfect complementarity to the transcripts of animal protein-coding genes (also termed targets) generally within the 3’ untranslated region (3’ UTR). The existing target prediction programs typically rely on a combination of specific base-pairing rules in the miRNA and target mRNA sequences, and conservational analysis to score possible 3’ UTR recognition sites and enumerate putative gene targets. These methods often produce a large number of false positive predictions. In this chapter, Masulli et al. improve the performance of an existing tool called miRanda by exploiting the updated information on biologically validated miRNA gene targets related to human prostate cancer only, and performing automatic parameter tuning using genetic algorithm.

Chapters 6–10 constitute the third part of the book dealing with structural analysis. Chapter 6 deals with the structural search in RNA motif databases. An RNA structural motif is a substructure of an RNA molecule that has a significant biological function. In this chapter, Wen and Wang present two recently developed structural search engines. These are useful to scientists and researchers who are interested in RNA secondary structure motifs. The first search engine is installed on a database, called RmotifDB, which contains secondary structures of the noncoding RNA sequences in Rfam. The second search engine is installed on a block database, which contains the 603 seed alignments, also called blocks, in Rfam. This search engine employs a novel tool, called BlockMatch, for comparing multiple sequence alignments. Some experimental results are reported to demonstrate the effectiveness of the BlockMatch tool.

In Chapter 7, Bhattacharya et al. explore the construction of neighborhood-based kernels on protein structures. Two types of neighborhoods, and two broad classes of kernels, namely, sequence and structure based, are defined. Ways of combining these kernels to get kernels on neighborhoods are discussed. Detailed experimental results are reported showing that some of the designed kernels perform competitively with the state of the art structure comparison algorithms, on the difficult task of classifying 40%sequence nonredundant proteins into SCOP superfamilies.

The use of protein blocks to characterize structural variations in enzymes is discussed in Chapter 8 using kinases as the case study. A protein block is a set of 16 local structural descriptors that has been derived using unsupervised machine learning algorithms and that can approximate the three-dimensional space of proteins. In this chapter, Agarwal et al. first apply their approach in distinguishing between conformation changes and rigid-body displacements between the structures of active and inactive forms of a kinase. Second, a comparison of the conformational patterns of active forms of a kinase with the active and inactive forms of a closely related kinase has been performed. Finally, structural differences in the active states of homologous kinases have been studied. Such studies might help in understanding the structural differences among these enzymes at a different level, as well as guide in making drug targets for a specific kinase.

In Chapter 9, Smalter and Huan address the problem of graph classification through the study of kernel functions and the application of graph classification in chemical quantitative structure–activity relationship (QSAR) study. Graphs, especially the connectivity maps, have been used for modeling chemical structures for decades. In connectivity maps, nodes represent atoms and edges represent chemical bonds between atoms. Support vector machines (SVMs) that have gained popularity in drug design and cheminformatics are used in this regard. Some graph kernel functions are explored that improve on existing methods with respect to both classification accuracy and kernel computation time. Experimental results are reported on five different biological activity data sets, in terms of the classifier prediction accuracy of the support vector machine for different feature generation methods.

Computational ligand design is one of the promising recent approaches to address the problem of drug discovery. It aims to search the chemical space to find suitable drug molecules. In Chapter 10, genetic algorithms have been applied for this combinatorial problem of ligand design. The chapter proposes a variable length genetic algorithm for de novo ligand design. It finds the active site of the target protein from the input protein structure and computes the bond stretching, angle bending, angle rotation, van der Waals, and electrostatic energy components using the distance dependent dielectric constant for assigning the fitness score for every individual. It uses a library of 41 fragments for constructing ligands. Ligands have been designed for two different protein targets, namely, Thrombin and HIV-1 Protease. The ligands obtained, using the proposed algorithm, were found to be similar to the real known inhibitors of these proteins. The docking energies using the proposed methodology designed were found to be lower compared to three existing approaches.

Chapters 11–13 constitute the fourth part of the book dealing with microarray data analysis. In Chapter 11, Saha and Maulik develop a differential evolution-based fuzzy clustering algorithm (DEFC) and apply it on four publicly available benchmark microarray data sets, namely, yeast sporulation, yeast cell cycle, Arabidopsis Thaliana, and human fibroblasts serum. Detailed comparative results demonstrating the superiority of the proposed approach are provided. In a part of the investigation, an interesting study integrating the proposed clustering approach with an SVM classifier has been conducted. A fraction of the data points is selected from different clusters based on their proximity to the respective centers. This is used for training an SVM. The clustering assignments of the remaining points are thereafter determined using the trained classifier. Finally, a biological significance test has been carried out on yeast sporulation microarray data to establish that the developed integrated technique produces functionally enriched clusters.

The classification capability of SVMs is again used in Chapter 12 for identifying potential gene markers that can distinguish between malignant and benign samples in different types of cancers. The proposed scheme consists of two phases. In the first, an ensemble of SVMs using different kernel functions is used for efficient classification. Thereafter, the signal-to-noise ratio statistic is used to select a number of gene markers, which is further reduced by using a multiobjective genetic algorithm-based feature selection method. Results are demonstrated on three publicly available data sets.

In Chapter 13, Maulik and Sarker develop a parallel algorithm for clustering gene expression data that exploits the property of symmetry of the clusters. It is based on a recently developed symmetry-based distance measure. The bottleneck for the application of such an approach for microarray data analysis is the large computational time. Consequently, Maulik and Sarker develop a parallel implementation of the symmetry-based clustering algorithm. Results are demonstrated for one artificial and four benchmark microarray data sets.

The last part of the book, dealing with topics related to systems biology, consists of Chapters 14–16. Jeong and Chen deal with the problem of gene prioritization in Chapter 14, which aims at achieving a better understanding of the disease process and to find therapy targets and diagnostic biomarkers. Gene prioritization is a new approach for extending our knowledge about diseases and potentially about other biological conditions. Jeong and Chen review the existing methods of gene prioritization and attempt to identify those that were most successful. They also discuss the remaining challenges and open problems in this area.

In Chapter 15, Bagchi discusses the various aspects of protein–protein interactions (PPI) that are one of the central players in many vital biochemical processes. Emphasis has been given to the properties of the PPI. A few basic definitions have been revisited. Several computational PPI prediction methods have been reviewed. The various software tools involved have also been reviewed.

Finally, in Chapter 16, Bhattacharyya and Bandyopadhyay study PPI networks in order to investigate the system level activities of the genotypes. Several topological properties and structures have been discussed and state-of-the-art knowledge on utilizing these characteristics in a system level study is included. A novel method of mining an integrated network, obtained by combining two types of topological properties, is designed to find dense subnetworks of proteins that are functionally coherent. Some theoretical analysis on the formation of dense subnetworks in a scale-free network is also provided. The results on PPI information of Homo Sapiens, obtained from the Human Protein Reference Database, show promise with such an integrative approach of topological analysis.

The field of biological informatics is rapidly evolving with the availability of new methods of data collection that are not only capable of collecting huge amounts of data, but also produce new data types. In response, advanced methods of searching for useful regularities or patterns in these data sets have been developed. Computational intelligence, comprising a wide array of classification, optimization, and representation methods, have found particular favor among the researchers in biological informatics. The chapters dealing with the applications of computational intelligence and pattern analysis techniques in biological informatics provide a representative view of the available methods and their evaluation in real domains. The volume will be useful to graduate students and researchers in computer science, bioinformatics, computational and molecular biology, biochemistry, systems science, and information technology both as a text and reference book for some parts of the curriculum. The researchers and practitioners in industry, including pharmaceutical companies, and R & D laboratories will also benefit from this book.

We take this opportunity to thank all the authors for contributing chapters related to their current research work that provide the state of the art in advanced computational intelligence and pattern analysis methods in biological informatics. Thanks are due to Indrajit Saha and Malay Bhattacharyya who provided technical support in preparing this volume, as well as to our students who have provided us the necessary academic stimulus to go on. Our special thanks goes to Anirban Mukhopadhyay for his contribution to the book and Christy Michael from Aptara Inc. for her constant help. We are also grateful to Michael Christian of John Wiley & Sons for his constant support.

U. MAULIK, S. BANDYOPADHYAY, AND J. T. L. WANG

November, 2009

CONTRIBUTORS

Ajith Abraham, Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, Washington

G. Agarwal, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India

Angshuman Bagchi, Buck Institute for Age Research, 8001 Redwood Blvd., Novato, California

Sanghamitra Bandyopadhyay, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

Chiranjib Bhattacharyya, Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India

Malay Bhattacharyya, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

Sourangshu Bhattacharya, Department of Computer Science and Automation, Indian Institute of Science Bangalore, India

S. Durga Bhavani, Department of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

Kevin Byron, Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey

Miguel Cervantes-Cervantes, Department of Biological Sciences, Rutgers University, Newark, New Jersey

Basabi Chakraborty, Department of Software and Information Science, Iwate Prefectural University, Iwate, Japan

Nagasuma R., Chandra Bioinformatics Center, Indian Institute of Science, Bangalore, India

Jake Y., Chen School of Informatics, Indiana University-Purdue University, Indianapolis, Indiana

Swagatam Das, Department of Electronics and Telecommunication, Jadavpur University, Kolkata, India

Alexandre G. de Brevern, Université Paris Diderot-Paris, Institut National de Transfusion Sanguine (INTS), Paris, France

D. C. Dinesh, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India

Jun Huan, Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, Kansas

Jieun Jeong, School of Informatics, Indiana University-Purdue University, Indianapolis, Indiana

Francesco Masulli, Department of Computer and Information Sciences, University of Genova, Italy

Ujjwal Maulik, Depatment of Computer Science and Engineering, Jadavpur University, Kolkata, India

Anirban Mukhopadhyay, Department of Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany, on leave from Department of Computer Science and Engineering, University of Kalyani, India

B. K. Panigrahi, Department of Electrical Engineering, Indian Institute of Technology (IIT), Delhi, India

S. Bapi Raju, Department of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

T. Sobha Rani, Department of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

Stefano Rovetta, Department of Computer and Information Sciences, University of Genova, Italy

Giuseppe Russo, Sbarro Institute for Cancer Research and Molecular Medicine, Temple University, Philadelphia, Pennsylvania

Indrajit Saha, Interdisciplinary Centre for Mathematical and Computational Modeling, University of Warsaw, Poland

Anasua Sarkar, LaBRI, University Bordeaux 1, France

Soumi Sengupta, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

Aaron Smalter, Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, Kansas

N. Srinivasan, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India

Jason T. L. Wang, Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey

Dongrong Wen, Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey

PART I

INTRODUCTION

CHAPTER 1

COMPUTATIONAL INTELLIGENCE: FOUNDATIONS, PERSPECTIVES, AND RECENT TRENDS

SWAGATAM DAS, AJITH ABRAHAM, AND B. K. PANIGRAHI

The field of computational intelligence has evolved with the objective of developing machines that can think like humans. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, understanding, learning, and so on. Computational intelligence includes neural networks, fuzzy inference systems, global optimization algorithms, probabilistic computing, swarm intelligence, and so on. This chapter introduces the fundamental aspects of the key components of modern computational intelligence. It presents a comprehensive overview of various tools of computational intelligence (e.g., fuzzy logic, neural network, genetic algorithm, belief network, chaos theory, computational learning theory, and artificial life). The synergistic behavior of the above tools on many occasions far exceeds their individual performance. A discussion on the synergistic behavior of neuro-fuzzy, neuro-genetic algorithms (GA), neuro-belief, and fuzzy-belief network models is also included in the chapter.

1.1 WHAT IS COMPUTATIONAL INTELLIGENCE?

Machine Intelligence refers back to 1936, when Turing proposed the idea of a universal mathematics machine [1,2], a theoretical concept in the mathematical theory of computability. Turing and Post independently proved that determining the decidability of mathematical propositions is equivalent to asking what sorts of sequences of a finite number of symbols can be recognized by an abstract machine with a finite set of instructions. Such a mechanism is now known as a Turing machine [3]. Turing’s research paper addresses the question of machine intelligence, assessing the arguments against the possibility of creating an intelligent computing machine and suggesting answers to those arguments, proposing the Turing test as an empirical test of intelligence [4]. The Turing test, called the imitation game by Turing, measures the performance of a machine against that of a human being. The machine and a human (A) are placed in two rooms. A third person, designated the interrogator, is in a room apart from both the machine and the human (A). The interrogator cannot see or speak directly to either (A) or the machine, communicating with them solely through some text messages or even a chat window. The task of the interrogator is to distinguish between the human and the computer on the basis of questions he/she may put to both of them over the terminals. If the interrogator cannot distinguish the machine from the human then, Turing argues, the machine may be assumed to be intelligent. In the 1960s, computers failed to pass the Turing test due to the low-processing speed of the computers.

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