Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation

Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation

Wei-Chiang Samuelson Hong

Copyright: © 2013 |Pages: 357

ISBN13: 9781466636286|ISBN10: 1466636289|EISBN13: 9781466636293

DOI: 10.4018/978-1-4666-3628-6

Cite Book Cite Book

MLA

Samuelson Hong, Wei-Chiang, editor. Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation. IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3628-6

APA

Samuelson Hong, W. (Ed.). (2013). Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation. IGI Global. https://doi.org/10.4018/978-1-4666-3628-6

Chicago

Samuelson Hong, Wei-Chiang, ed. Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3628-6

Export Reference

Favorite Full-Book Download

Evolutionary computation has emerged as a major topic in the scientific community as many of its techniques have successfully been applied to solve problems in a wide variety of fields.

Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation provides comprehensive research on emerging theories and its aspects on intelligent computation. Particularly focusing on breaking trends in evolutionary computing, algorithms, and programming, this publication serves to support professionals, government employees, policy and decision makers, as well as students in this scientific field.

Table of Contents

Reset

Front Materials

Title Page

Copyright Page

Editorial Advisory Board

Preface

Chapters

Chapter 1

Reliability Allocation Problem in Series-Parallel Systems: Ant Colony Optimization (pages 1-15)

Alice Yalaoui, Farah Belmecheri, Eric Châtelet, Farouk Yalaoui

Reliability optimization is an important step in industrial systems design. In order to develop a reliable system, designers may introduce different redundant technologies with the same functionality in parallel. In this paper, each...

Chapter 2

Feature Selection Based on Minimizing the Area Under the Detection Error Tradeoff Curve (pages 16-31)

Liau Heng Fui, Dino Isa

Feature selection is crucial to select an “optimized” subset of features from the original feature set based on a certain objective function. In general, feature selection removes redundant or irrelevant data while retaining...

Chapter 3

Application of Genetic Algorithm to Minimize the Number of Objects Processed and Setup in a One-Dimensional Cutting Stock Problem (pages 32-45)

Julliany Sales Brandão, Alessandra Martins Coelho, João Flávio V. Vasconcellos, Luiz Leduíno de Salles Neto, André Vieira Pinto

This paper presents the application of the one new approach using Genetic Algorithm in solving One-Dimensional Cutting Stock Problems in order to minimize two objectives, usually conflicting, i.e., the number of processed objects and...

Chapter 4

A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets (pages 46-53)

Periasamy Vivekanandan, Raju Nedunchezhian

Genetic algorithm is a search technique purely based on natural evolution process. It is widely used by the data mining community for classification rule discovery in complex domains. During the learning process it makes several...

Chapter 5

Simultaneous Tolerance Synthesis for Manufacturing and Quality using Evolutionary Algorithms (pages 54-72)

Y. S. Rao, C. S. P. Rao, G. Ranga Janardhana, Pandu R. Vundavilli

Tolerance plays a major role in the manufacturing industry, as it affects product design, manufacturing, and quality of the product. This paper considers product design, manufacturing, and quality simultaneously, and introduces a...

Chapter 6

Parallel Single and Multiple Objectives Genetic Algorithms: A Survey (pages 73-110)

B. S. P. Mishra, S. Dehuri, R. Mall, A. Ghosh

This paper critically reviews the reported research on parallel single and multi-objective genetic algorithms. Many early efforts on single and multi-objective genetic algorithms were introduced to reduce the processing time needed...

Chapter 7

Experimental Study on Recent Advances in Differential Evolution Algorithm (pages 111-133)

G. Jeyakumar, C. Shanmugavelayutham

The Differential Evolution (DE) is a well known Evolutionary Algorithm (EA), and is popular for its simplicity. Several novelties have been proposed in research to enhance the performance of DE. This paper focuses on demonstrating...

Chapter 8

The Volatility for Pre and Post Global Financial Crisis: An Application of Computational Finance (pages 134-148)

Shih-Yung Wei, Jack J. W. Yang, Jen-Tseng Chen, Wei-Chiang Hong

The asymmetric volatility, temporary volatility, and permanent volatility of financial asset returns have attracted much interest in recent years. However, a consensus has not yet been reached on the causes of them for both the...

Chapter 9

Differential Operators Embedded Artificial Bee Colony Algorithm (pages 149-163)

Tarun Kumar Sharma, Millie Pant

Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization...

Chapter 10

Appropriate Evolutionary Algorithm for Scheduling in FMS (pages 164-175)

B. B. Choudhury, B. B. Biswal, D. Mishra, R. N. Mahapatra

The diffusion of flexible manufacturing systems (FMS) has not only invigorated production systems, but has also given considerable impetus to relevant analytical fields like scheduling theory and adaptive controls. Depending on the...

Chapter 11

GBF Trained Neuro-Fuzzy Equalizer for Time Varying Channels (pages 176-188)

Archana Sarangi, Sasmita Kumari Padhy, Siba Prasada Panigrahi, Shubhendu Kumar Sarangi

This paper proposes a neuro-fuzzy filter for equalization of time-varying channels. Additionally, it proposes to tune the equalizer with a hybrid algorithm between Genetic Algorithms (GA) and Bacteria Foraging (BFO), termed as GBF....

Chapter 12

An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting (pages 189-205)

S. Chakravarty, P. K. Dash, V. Ravikumar Pandi, B. K. Panigrahi

This paper proposes a hybrid model, evolutionary functional link neural fuzzy model (EFLNF), to forecast financial time series where the parameters are optimized by two most efficient evolutionary algorithms: (a) genetic algorithm...

Chapter 13

Firm Size Transmission Effect and Price-Volume Relationship Analysis During Financial Tsunami Periods (pages 206-225)

Shih-Yung Wei, Wei-Chiang Hong, Kai Wang

Investors attend importance to forecast the price of financial assets, thus, the factors affecting the stock price are usually the focus of financial research in the field, in which the most important factors to scholars are firm...

Chapter 14

Recursive Learning of Genetic Algorithms with Task Decomposition and Varied Rule Set (pages 226-247)

Lei Fang, Sheng-Uei Guan, Haofan Zhang

Rule-based Genetic Algorithms (GAs) have been used in the application of pattern classification (Corcoran & Sen, 1994), but conventional GAs have weaknesses. First, the time spent on learning is long. Moreover, the classification...

Chapter 15

LZW Encoding in Genetic Algorithm (pages 248-259)

Worasait Suwannik

To solve a problem using Genetic Algorithms (GAs), a solution must be encoded into a binary string. The length of the binary string represents the size of the problem. As the length of the binary string increases, the size of the...

Chapter 16

Usage Profile Generation from Web Usage Data Using Hybrid Biclustering Algorithm (pages 260-272)

R. Rathipriya, K. Thangavel, J. Bagyamani

Biclustering has the potential to make significant contributions in the fields of information retrieval, web mining, and so forth. In this paper, the authors analyze the complex association between users and pages of a web site by...

Chapter 17

An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression (pages 273-289)

Jiansheng Wu

Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including...

Back Materials

Compilation of References

About the Contributors

Index

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account