Evolutionary Computing

Evolutionary Computing

Thomas E. Potok (Oak Ridge National Laboratory, USA), Xiaohui Cui (Oak Ridge National Laboratory, USA) and Yu Jiao (Oak Ridge National Laboratory, USA)
DOI: 10.4018/978-1-59904-982-3.ch008
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Abstract

The rate at which information overwhelms humans is significantly more than the rate at which humans have learned to process, analyze, and leverage this information. To overcome this challenge, new methods of computing must be formulated, and scientist and engineers have looked to nature for inspiration in developing these new methods. Consequently, evolutionary computing has emerged as new paradigm for computing, and has rapidly demonstrated its ability to solve real-world problems where traditional techniques have failed. This field of work has now become quite broad and encompasses areas ranging from artificial life to neural networks. This chapter specifically focuses on two sub-areas of nature-inspired computing: Evolutionary Algorithms and Swarm Intelligence.
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Evolutionary Algorithms

Charles Darwin radically changed the way evolutionary biology is viewed in his work entitled “Origin of Species” published in 1859 (Darwin, 1859). In this work, Darwin describes his theory of natural selection based on his experience and observations of nature around the world. Darwin states that there is an implicit struggle for survival because of species producing more offspring than can grow to adulthood and that food sources are limited. Because of this implicit struggle, sexually reproducing species create offspring that are genetic variants of the parents. Darwin theorizes that it is this genetic variation that enables some offspring to survive in a particular environment much better than other offspring with different genetic variations. As a direct result of this “enhanced” genetic variation, these offspring not only survive in the environment, but go on to reproduce new offspring that carry some form of this enhanced genetic variation. In addition, those offspring that are not as suited for the environment do not pass on their genetic variation to offspring, but rather die off. Darwin then theorizes that over many generations of reproduction, new species that are highly adapted to their specific environments will emerge. It is this theory of natural selection that forms the theoretical foundation for the field of Evolutionary Algorithms (EA).

Following in the footsteps of Darwin, John Holland dramatically altered the computer science and artificial intelligence fields in 1975 with his publication entitled “Adaptation in Natural and Artificial Systems” (Holland 1975). In this work, Holland describes a mathematical model for the evolutionary process of natural selection, and demonstrates its use in a variety of problem domains. This seminal work by Holland created the fertile soil by which the field of Evolutionary Algorithms grew and thrived. In the same year and under the direction of Holland, Ken De Jong’s dissertation entitled “An Analysis of the Behavior of a Class of Genetic Adaptive Systems” helps fully demonstrate the possibilities of using evolutionary algorithms for problem solving (De Jong, 1975). In 1989, the field of evolutionary algorithms received a fresh injection of enthusiasm with the publication of David Goldberg’s work entitled “Genetic Algorithms in Search, Optimization, and Machine Learning” (Goldberg, 1989). The momentum of development continued with Melanie Mitchell’s 1996 work entitled “An Introduction to Genetic Algorithms,” which helped to further solidify the theoretical foundations of EAs (Mitchell, 1996). Ever since then, the field has continued to grown and the practical applications of EA’s are abounding with success stories (Chambers, 2000; Coley, 2001; Haupt, 1998).

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Editorial Advisory Board
Table of Contents
Preface
Hsiao-Fan Wang
Acknowledgment
Hsiao-Fan Wang
Chapter 1
Martin Spott, Detlef Nauck
This chapter introduces a new way of using soft constraints for selecting data analysis methods that match certain user requirements. It presents a... Sample PDF
Automatic Intelligent Data Analysis
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Chapter 2
Hung T. Nguyen, Vladik Kreinovich, Gang Xiang
It is well known that in decision making under uncertainty, while we are guided by a general (and abstract) theory of probability and of statistical... Sample PDF
Random Fuzzy Sets: Theory & Applications
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Chapter 3
Gráinne Kerr, Heather Ruskin, Martin Crane
Microarray technology1 provides an opportunity to monitor mRNA levels of expression of thousands of genes simultaneously in a single experiment. The... Sample PDF
Pattern Discovery in Gene Expression Data
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Chapter 4
Erica Craig, Falk Huettmann
The use of machine-learning algorithms capable of rapidly completing intensive computations may be an answer to processing the sheer volumes of... Sample PDF
Using "Blackbox" Algorithms Such AS TreeNET and Random Forests for Data-Ming and for Finding Meaningful Patterns, Relationships and Outliers in Complex Ecological Data: An Overview, an Example Using G
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Chapter 5
Eulalia Szmidt, Marta Kukier
We present a new method of classification of imbalanced classes. The crucial point of the method lies in applying Atanassov’s intuitionistic fuzzy... Sample PDF
A New Approach to Classification of Imbalanced Classes via Atanassov's Intuitionistic Fuzzy Sets
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Chapter 6
Arun Kulkarni, Sara McCaslin
This chapter introduces fuzzy neural network models as means for knowledge discovery from databases. It describes architectures and learning... Sample PDF
Fuzzy Neural Network Models for Knowledge Discovery
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Chapter 7
Ivan Bruha
This chapter discusses the incorporation of genetic algorithms into machine learning. It does not present the principles of genetic algorithms... Sample PDF
Genetic Learning: Initialization and Representation Issues
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Chapter 8
Evolutionary Computing  (pages 131-142)
Thomas E. Potok, Xiaohui Cui, Yu Jiao
The rate at which information overwhelms humans is significantly more than the rate at which humans have learned to process, analyze, and leverage... Sample PDF
Evolutionary Computing
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Chapter 9
M. C. Bartholomew-Biggs, Z. Ulanowski, S. Zakovic
We discuss some experience of solving an inverse light scattering problem for single, spherical, homogeneous particles using least squares global... Sample PDF
Particle Identification Using Light Scattering: A Global Optimization Problem
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Chapter 10
Dominic Savio Lee
This chapter describes algorithms that use Markov chains for generating exact sample values from complex distributions, and discusses their use in... Sample PDF
Exact Markov Chain Monte Carlo Algorithms and Their Applications in Probabilistic Data Analysis and Inference
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Chapter 11
J. P. Ganjigatti, Dilip Kumar Pratihar
In this chapter, an attempt has been made to design suitable knowledge bases (KBs) for carrying out forward and reverse mappings of a Tungsten inert... Sample PDF
Design and Development of Knowledge Bases for Forward and Reverse Mappings of TIG Welding Process
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Chapter 12
Malcolm J. Beynon
This chapter considers the role of fuzzy decision trees as a tool for intelligent data analysis in domestic travel research. It demonstrates the... Sample PDF
A Fuzzy Decision Tree Analysis of Traffic Fatalities in the US
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Chapter 13
Dymitr Ruta, Christoph Adl, Detlef Nauck
In the telecom industry, high installation and marketing costs make it six to 10 times more expensive to acquire a new customer than it is to retain... Sample PDF
New Churn Prediction Strategies in the Telecom Industry
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Chapter 14
Malcolm J. Beynon
This chapter demonstrates intelligent data analysis, within the environment of uncertain reasoning, using the recently introduced CaRBS technique... Sample PDF
Intelligent Classification and Ranking Analyses Using CARBS: Bank Rating Applications
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Chapter 15
Fei-Chen Hsu, Hsiao-Fan Wang
In this chapter, we used Cumulative Prospect Theory to propose an individual risk management process (IRM) including a risk analysis stage and a... Sample PDF
Analysis of Individual Risk Attitude for Risk Management Based on Cumulative Prospect Theory
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Chapter 16
Francesco Giordano, Michele La Rocca, Cira Perna
This chapter introduces the use of the bootstrap in a nonlinear, nonparametric regression framework with dependent errors. The aim is to construct... Sample PDF
Neural Networks and Bootstrap Methods for Regression Models with Dependent Errors
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Chapter 17
Lean Yu, Shouyang Wang, Kin Keung Lai
Financial crisis is a kind of typical rare event, but it is harmful to economic sustainable development if occurs. In this chapter, a... Sample PDF
Financial Crisis Modeling and Prediction with a Hilbert-EMD-Based SVM Approachs
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Chapter 18
Chun-Jung Huang, Hsiao-Fan Wang, Shouyang Wang
One of the key problems in supervised learning is due to the insufficient size of the training data set. The natural way for an intelligent learning... Sample PDF
Virtual Sampling with Data Construction Analysis
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About the Contributors