Contents

#### **Preface XIII**

**Part 1 Recent Development of Genetic Algorithm 1**  Chapter 1 **The Successive Zooming Genetic Algorithm and Its Applications 3**  Young-Doo Kwon and Dae-Suep Lee Chapter 2 **The Network Operator Method for Search of the Most Suitable Mathematical Equation 19**  Askhat Diveev and Elena Sofronova Chapter 3 **Performance of Simple Genetic Algorithm Inserting Forced Inheritance Mechanism and Parameters Relaxation 43**  Esther Lugo-González, Emmanuel A. Merchán-Cruz, Luis H. Hernández-Gómez, Rodolfo Ponce-Reynoso, Christopher R. Torres-San Miguel and Javier Ramírez-Gordillo Chapter 4 **The Roles of Crossover and Mutation in Real-Coded Genetic Algorithms 65**  Yourim Yoon and Yong-Hyuk Kim Chapter 5 **A Splicing/Decomposable Binary Encoding and Its Novel Operators for Genetic and Evolutionary Algorithms 83**  Yong Liang Chapter 6 **Genetic Algorithms: An Overview with Applications in Evolvable Hardware 105**  Popa Rustem **Part 2 New Applications of Genetic Algorithm 121**  Chapter 7 **Tune Up of a Genetic Algorithm to Group Documentary Collections 123** 

José Luis Castillo Sequera

X Contents


Contents VII

Chapter 19 **A Stochastically Perturbed Particle Swarm Optimization** 

**Part 4 Hybrid Bio-Inspired Computational Algorithms 383** 

**Based on Genetic Algorithm with Single and** 

Chapter 21 **Using a Genetic Algorithm to Solve the Benders' Master Problem for Capacitated Plant Location 405**

Deam James Azevedo da Silva, Otávio Noura Teixeira

Mehmet Sevkli and Aise Zulal Sevkli

Chapter 20 **Performance Study of Cultural Algorithms** 

**Multi Population for the MKP 385**

and Roberto Célio Limão de Oliveira

Ming-Che Lai and Han-suk Sohn

**for Identical Parallel Machine Scheduling Problems 371** 

	- **Part 3 Artificial Immune Systems and Swarm Intelligence 333**
	- **Part 4 Hybrid Bio-Inspired Computational Algorithms 383**

VI Contents

Chapter 8 **Public Portfolio Selection Combining Genetic Algorithms and Mathematical Decision Analysis 139**  Eduardo Fernández-González, Inés Vega-López

Chapter 9 **The Search for Parameters and Solutions: Applying Genetic Algorithms on Astronomy and Engineering 161** 

> **Automated Linear Modelling of Time Series 213** Pedro Flores, Larysa Burtseva and Luis B. Morales

**Genetic Algorithms and FLDR in a Restricted-Vocabulary** 

Kim Soon Gan, Patricia Anthony, Jason Teo and Kim On Chin

Julio César Martínez-Romo, Francisco Javier Luna-Rosas, Miguel Mora-González, Carlos Alejandro de Luna-Ortega

**Algorithm Techniques in Online Auction 263** 

**Data Stream Based on Genetic Algorithm 291**

Chapter 16 **On the Application of Optimal PWM of Induction Motor in Synchronous Machines at High Power Ratings 317** 

**Part 3 Artificial Immune Systems and Swarm Intelligence 333** 

Pedro Rocha, Alexandre Pigozzo, Bárbara Quintela, Gilson Macedo,

Chapter 17 **Artificial Immune Systems, Dynamic Fitness Landscapes, and the Change Detection Problem 335** 

and Jorge Navarro-Castillo

Chapter 10 **Fusion of Visual and Thermal Images**

Chapter 11 **Self Adaptive Genetic Algorithms for** 

Chapter 12 **Optimal Feature Generation with** 

and Valentín López-Rivas

Chapter 14 **Mining Frequent Itemsets over Recent** 

Zhou Yong, Han Jun and Guo He

Chapter 15 **Optimal Design of Power System Controller** 

Arash Sayyah and Alireza Rezazadeh

Chapter 18 **Modelling the Innate Immune System 351**

Rodrigo Santos and Marcelo Lobosco

K. A. Folly and S. P. Sheetekela

Hendrik Richter

**Using Breeder Genetic Algorithm 303** 

Chapter 13 **Performance of Varying Genetic** 

**Using Genetic Algorithms 187**  Sertan Erkanli, Jiang Li and Ender Oguslu

**Speech Recognition System 235**

Annibal Hetem Jr.

Chapter 21 **Using a Genetic Algorithm to Solve the Benders' Master Problem for Capacitated Plant Location 405**  Ming-Che Lai and Han-suk Sohn

Preface

technologies.

In recent years, there has been a growing interest in the use of biology as a source of inspiration for solving practical problems. These emerging techniques are often referred to as "bio-inspired computational algorithms". The purpose of bio-inspired computational algorithms is primarily to extract useful metaphors from natural biological systems. Additionally, effective computational solutions to complex problems in a wide range of domain areas can be created. The more notable developments have been the genetic algorithm (GA) inspired by neo-Darwinian theory of evolution, the artificial immune system (AIS) inspired by biological immune principles, and the swarm intelligence (SI) inspired by social behavior of gregarious insects and other animals. It has been demonstrated in many areas that the bioinspired computational algorithms are complementary to many existing theories and

In this research book, a small collection of recent innovations in bio-inspired computational algorithms is presented. The techniques covered include genetic algorithms, artificial immune systems, particle swarm optimization, and hybrid models. Twenty-four chapters are contained, written by leading experts from researchers of computational intelligence communities, practitioners from industrial engineering, the Air Force Academy, and mechanical engineering. The objective of this book is to present an international forum for the synergy of new developments from different research disciplines. It is hoped, through the fusion of diverse techniques and

This book is organized into four sections. The first section shows seven innovative works that give a flavor of how genetic algorithms can be improved from different aspects. In Chapter 1, a sophisticated variant of genetic algorithms was presented. The characteristic of the proposed successive zooming genetic algorithm was that it can predict the possibility of the solution found to be an exact optimum solution which aims to accelerate the convergent speed of the algorithm. In the second chapter, based on the newly introduced data structure named "network operator", a genetic algorithm was used to search the structure of an appropriate mathematical expression and its parameters. In the third chapter, two kinds of newly developed mechanisms were incorporated into genetic algorithms for optimizing the trajectories generation in closed chain mechanisms, and planning the effects that it had on the mechanism by

applications, that new and innovative ideas will be stimulated and shared.
