Preface

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 technologies.

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

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 relaxing some parameters. These two mechanisms are as follows: the forced inheritance mechanism and the regeneration mechanism. The fourth chapter examines an empirical investigation on the roles of crossover and mutation operators in real-coded genetic algorithms. The fifth chapter summarizes custom processing architectures for genetic algorithms, and it presents a proposal for a scalable parallel array, which is adequate enough for implementation on field-programmable gate array technology. In the sixth chapter, a novel genetic algorithm with splicing and decomposable encoding representation was proposed. One very interesting characteristic of this representation is that it can be spliced and decomposed to describe potential solutions of the problem with different precisions by different numbers of uniform-salient building-blocks. Finally, a comprehensive overview on genetic algorithms, including the algorithm history, the algorithm architecture, a classification of genetic algorithms, and applications on evolvable hardware as examples were well summarized in the seventh chapter.

Preface XI

**Shangce Gao**

Shanghai

Associate Research Fellow

Ministry of Education Tongji University

change detection problem in dynamic fitness landscapes. In the 19th chapter, the dynamics of the innate immune response to Lipopolysaccharide in a microscopic section of tissue were formulated and modelled, using a set of partial differential equations. The 20th chapter analyzes swarm intelligence, i.e. the particle swarm optimization was used to deal with the identical parallel machine scheduling problem. The main characteristic of the algorithm was that its search strategy is perturbed by

The fourth section includes four hybrid models by combing different meta-heuristics. Hybridization is nowadays recognized to be an essential aspect of high performing algorithms. Pure algorithms are always inferior to hybridizations. This section shows good examples of hybrid models. In the 21st chapter, three immune functions (immune memory, antibody diversity, and self-adjusting) were incorporated into the genetic algorithm to quicken its search speed and improve its local/global search capacity. The 22nd chapter focuses on the combination of genetic algorithm and culture algorithm. Performance on multidimensional knapsack problem verified the effectiveness of the hybridization. Chapter 23 studies the genetic algorithm that was incorporated into the Benders' Decomposition Algorithm to solve the capacitated plant location problem. To solve the constrained multiple-objective supply chain optimization problem, two bioinspired algorithms, involving a non-dominated sorting genetic algorithm and a novel multi-objective particle swarm optimizer, were investigated and compared in the 24th

Because the chapters are written by many researchers with different backgrounds around the world, the topics and content covered in this book provides insights which are not easily accessible otherwise. It is hoped that this book will provide a reference to researchers, practicing professionals, undergraduates, as well as graduate students

The editor would like to express his utmost gratitude and appreciation to the authors for their contributions. Thanks are also due to the excellent editorial assistance by the

The Key Laboratory of Embedded System and Service Computing,

in artificial intelligence communities for the benefit of more creative ideas.

stochastic factors.

chapter.

staff at InTech.

The second section is devoted to ten different real world problems that can be addressed by adapted genetic algorithms. The eighth chapter shows an effective clustering tool based on genetic algorithms to group documentary collections, and suggested taxonomy of parameters of the genetic algorithm numerical and structural. To solve a well-defined project portfolio selection problem, a hybrid model was presented in the ninth chapter by combining the genetic algorithm and functionalnormative (multi-criteria) approach. In the 10th chapter, wide applications on astrophysics, rocket engine engineering, and energy distribution of genetic algorithms were illustrated.These applications proposed a new formal methodology (i.e., the inverted model of input problems) when using genetic algorithms to solve the abundances problems. In the 11th chapter, a continuous genetic algorithm was investigated to integrate a pair of registered and enhanced visual images with an infrared image. The 12th chapter showed a very efficient and robust self-adaptive genetic algorithm to build linear modeling of time series. To deal with the restricted vocabulary speech recognition problem, the 13th chapter presented a novel method based on the genetic algorithm and the fisher's linear discriminate ratio (FLDR). The genetic algorithm was used to handle the optimal feature generation task, while FLDR acted as the separability criterion in the feature space. In the 14th chapter, a very interesting application of genetic algorithms under the dynamic online auctions environment was illustrated. The 15th chapter examines the use of a parallel genetic algorithm for finding frequent itemsets over recent data streams investigated, while a breeder genetic algorithm, used to design power system stabilizer for damping low frequency oscillations in power systems, was shown in the 16th chapter. The 17th chapter discusses genetic algorithms utilized to optimize pulse patterns in synchronous machines at high power ratings.

The third section compiles two artificial immune systems and a particle swarm optimization. The 18th chapter in the book proposes a negative selection scheme, which mimics the self/non-self discrimination of the natural immune system to solve the change detection problem in dynamic fitness landscapes. In the 19th chapter, the dynamics of the innate immune response to Lipopolysaccharide in a microscopic section of tissue were formulated and modelled, using a set of partial differential equations. The 20th chapter analyzes swarm intelligence, i.e. the particle swarm optimization was used to deal with the identical parallel machine scheduling problem. The main characteristic of the algorithm was that its search strategy is perturbed by stochastic factors.

X Preface

relaxing some parameters. These two mechanisms are as follows: the forced inheritance mechanism and the regeneration mechanism. The fourth chapter examines an empirical investigation on the roles of crossover and mutation operators in real-coded genetic algorithms. The fifth chapter summarizes custom processing architectures for genetic algorithms, and it presents a proposal for a scalable parallel array, which is adequate enough for implementation on field-programmable gate array technology. In the sixth chapter, a novel genetic algorithm with splicing and decomposable encoding representation was proposed. One very interesting characteristic of this representation is that it can be spliced and decomposed to describe potential solutions of the problem with different precisions by different numbers of uniform-salient building-blocks. Finally, a comprehensive overview on genetic algorithms, including the algorithm history, the algorithm architecture, a classification of genetic algorithms, and applications on evolvable hardware as

The second section is devoted to ten different real world problems that can be addressed by adapted genetic algorithms. The eighth chapter shows an effective clustering tool based on genetic algorithms to group documentary collections, and suggested taxonomy of parameters of the genetic algorithm numerical and structural. To solve a well-defined project portfolio selection problem, a hybrid model was presented in the ninth chapter by combining the genetic algorithm and functionalnormative (multi-criteria) approach. In the 10th chapter, wide applications on astrophysics, rocket engine engineering, and energy distribution of genetic algorithms were illustrated.These applications proposed a new formal methodology (i.e., the inverted model of input problems) when using genetic algorithms to solve the abundances problems. In the 11th chapter, a continuous genetic algorithm was investigated to integrate a pair of registered and enhanced visual images with an infrared image. The 12th chapter showed a very efficient and robust self-adaptive genetic algorithm to build linear modeling of time series. To deal with the restricted vocabulary speech recognition problem, the 13th chapter presented a novel method based on the genetic algorithm and the fisher's linear discriminate ratio (FLDR). The genetic algorithm was used to handle the optimal feature generation task, while FLDR acted as the separability criterion in the feature space. In the 14th chapter, a very interesting application of genetic algorithms under the dynamic online auctions environment was illustrated. The 15th chapter examines the use of a parallel genetic algorithm for finding frequent itemsets over recent data streams investigated, while a breeder genetic algorithm, used to design power system stabilizer for damping low frequency oscillations in power systems, was shown in the 16th chapter. The 17th chapter discusses genetic algorithms utilized to optimize pulse patterns in

The third section compiles two artificial immune systems and a particle swarm optimization. The 18th chapter in the book proposes a negative selection scheme, which mimics the self/non-self discrimination of the natural immune system to solve the

examples were well summarized in the seventh chapter.

synchronous machines at high power ratings.

The fourth section includes four hybrid models by combing different meta-heuristics. Hybridization is nowadays recognized to be an essential aspect of high performing algorithms. Pure algorithms are always inferior to hybridizations. This section shows good examples of hybrid models. In the 21st chapter, three immune functions (immune memory, antibody diversity, and self-adjusting) were incorporated into the genetic algorithm to quicken its search speed and improve its local/global search capacity. The 22nd chapter focuses on the combination of genetic algorithm and culture algorithm. Performance on multidimensional knapsack problem verified the effectiveness of the hybridization. Chapter 23 studies the genetic algorithm that was incorporated into the Benders' Decomposition Algorithm to solve the capacitated plant location problem. To solve the constrained multiple-objective supply chain optimization problem, two bioinspired algorithms, involving a non-dominated sorting genetic algorithm and a novel multi-objective particle swarm optimizer, were investigated and compared in the 24th chapter.

Because the chapters are written by many researchers with different backgrounds around the world, the topics and content covered in this book provides insights which are not easily accessible otherwise. It is hoped that this book will provide a reference to researchers, practicing professionals, undergraduates, as well as graduate students in artificial intelligence communities for the benefit of more creative ideas.

The editor would like to express his utmost gratitude and appreciation to the authors for their contributions. Thanks are also due to the excellent editorial assistance by the staff at InTech.

> **Shangce Gao** Associate Research Fellow The Key Laboratory of Embedded System and Service Computing, Ministry of Education Tongji University Shanghai

**Part 1** 

**Recent Development of Genetic Algorithm** 
