Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: . genetic algorithms, . differential evolution, . swarm intelligence, and . artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.

Autorentext
Xinjie Yu is an associate professor of the department of electrical engineering at Tsinghua University. He received his PhD in Electrical Engineering from Tsinghua University in 2001. Then he served as a lecturer at Tsinghua University until 2005 and was promoted to the position of associate professor; a role he has held ever since. He was a visiting scholar at the Massachusetts Institute of Technology in 2003 and at the Graduate School of Information, Production and Systems of Waseda University in 2008 and 2009 separately. Dr Yu's research interests include evolutionary computation (especially genetic algorithms, evolution strategy, multimodal optimization, and multiobjective optimization) and its applications in various aspects of electrical engineering, power electronics, wireless energy transferring, etc. Mitsuo Gen is a visiting scientist at the Fuzzy Logic Systems Institute (FLSI), Iizuka, Japan, which he joined in August 2009 after retiring from his position as a professor in the Graduate School of Information, Production and Systems, Waseda University; a role he had held since April 2003. He received a PhD in Engineering from Kogakuin University in 1974 and a PhD in Informatics from Kyoto University in 2006. He worked at Ashikaga Institute of Technology for several years: as a lecturer during the period 19741980, an associate professor during the period 19801987, and as a professor during the period 19872003. He was a visiting associate professor at the University of Nebraska-Lincoln from 19811982, and a visiting professor at the University of California at Berkeley from 1999-2000, at POSTECH in Fall 2008 and at the Asian Institute of Technology in Spring 2009. His research interests include genetic and evolutionary algorithms, artificial neural networks, fuzzy logic, and their applications to scheduling, network design, logistics systems, etc. He has authored several books, such as Genetic Algorithms and Engineering Design, (1997), GeneticAlgorithms and Engineering Optimization, (2000) with Dr. R. Cheng, and Network Models and Optimization: Multiobjective Genetic Algorithm Approach, Springer, London (2008) with Dr. R. Cheng and Dr. L. Lin. He has edited Intelligent and Evolutionary Systems, Studies in Computational Intelligence, vol. 187, Springer, Heidelberg (2009) with Dr. M. Gen et al., and has published more than 200 international journal papers. His books and papers have been cited more than 5000 times by researchers throughout the world.

Zusammenfassung

Evolutionary algorithms (EAs) are becoming increasingly attractive for researchers from various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science, economics, etc. This book presents an insightful, comprehensive, and up-to-date treatment of EAs, such as genetic algorithms, differential evolution, evolution strategy, constraint optimization, multimodal optimization, multiobjective optimization, combinatorial optimization, evolvable hardware, estimation of distribution algorithms, ant colony optimization, particle swarm optimization, artificial immune systems, artificial life, genetic programming, etc.

It emphasises the initiative ideas of the algorithm, contains discussions in the contexts, and suggests further readings and possible research projects. All the methods form a pedagogical way to make EAs easy and interesting.

This textbook also introduces the applications of EAs as many as possible. At least one real-life application is introduced by the end of almost every chapter. The authors focus on the kernel part of applications, such as how to model real-life problems, how to encode and decode the individuals, how to design effective search operators according to the chromosome structures, etc.

This textbook adopts pedagogical ways of making EAs easy and interesting. Its methods include an introduction at the beginning of each chapter, emphasising the initiative, discussions in the contexts, summaries at the end of every chapter, suggested further reading, exercises, and possible research projects.

Introduction to Evolutionary Algorithms will enable students to:

• establish a strong background on evolutionary algorithms;

• appreciate the cutting edge of EAs;

• perform their own research projects by simulating the application introduced in the book; and

• apply their intuitive ideas to academic search.

This book is aimed at senior undergraduate students or first-year graduate students as a textbook or self-study material.

Inhalt
Evolutionary Algorithms.- Simple Evolutionary Algorithms.- Advanced Evolutionary Algorithms.- Dealing with Complicated Problems.- Constrained Optimization.- Multimodal Optimization.- Multiobjective Optimization.- Combinatorial Optimization.- Brief Introduction to Other Evolutionary Algorithms.- Swarm Intelligence.- Artificial Immune Systems.- Genetic Programming.