EVOLUTIONARY ALGORITHMS Edited by Eisuke Kita Evolutionary Algorithms Edited by Eisuke Kita Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Katarina Lovrecic Technical Editor Teodora Smiljanic Cover Designer Martina Sirotic Image Copyright Designus, 2010. Used under license from Shutterstock.com First published March, 2011 Printed in India A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Evolutionary Algorithms, Edited by Eisuke Kita p. cm. ISBN 978-953-307-171-8 free online editions of InTech Books and Journals can be found at www.intechopen.com Part 1 Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Preface IX New Algorithms 1 Hybridization of Evolutionary Algorithms 3 Iztok Fister, Marjan Mernik and Janez Brest Linear Evolutionary Algorithm 27 Kezong Tang, Xiaojing Yuan, Puchen Liu and Jingyu Yang Genetic Algorithm Based on Schemata Theory 41 Eisuke Kita and Takashi Maruyama In Vitro Fertilization Genetic Algorithm 57 Celso G. Camilo-Junior and Keiji Yamanaka Bioluminescent Swarm Optimization Algorithm 69 Daniel Rossato de Oliveira, Rafael S. Parpinelli and Heitor S. Lopes A Memetic Particle Swarm Optimization Algorithm for Network Vulnerability Analysis 85 Mahdi Abadi and Saeed Jalili Quantum-Inspired Differential Evolutionary Algorithm for Permutative Scheduling Problems 109 Tianmin Zheng and Mitsuo Yamashiro Quantum-Inspired Particle Swarm Optimization for Feature Selection and Parameter Optimization in Evolving Spiking Neural Networks for Classification Tasks 133 Haza Nuzly Abdull Hamed, Nikola K. Kasabov and Siti Mariyam Shamsuddin Analytical Programming - a Novel Approach for Evolutionary Synthesis of Symbolic Structures 149 Ivan Zelinka, Donald Davendra, Roman Senkerik, Roman Jasek and Zuzana Oplatkova Contents Contents VI PPCea: A Domain-Specific Language for Programmable Parameter Control in Evolutionary Algorithms 177 Shih-Hsi Liu, Marjan Mernik, Mohammed Zubair, Matej Črepinšek and Barrett R. Bryant Evolution Algorithms in Fuzzy Data Problems 201 Witold Kosiński, Katarzyna Węgrzyn-Wolska and Piotr Borzymek Variants of Hybrid Genetic Algorithms for Optimizing Likelihood ARMA Model Function and Many of Problems 219 Basad Ali Hussain Al-Sarray and Rawa’a Dawoud Al-Dabbagh Tracing Engineering Evolution with Evolutionary Algorithms 247 Tino Stanković, Kalman Žiha and Dorian Marjanović Applications 269 Evaluating the α-Dominance Operator in Multiobjective Optimization for the Probabilistic Traveling Salesman Problem with Profits 271 Bingchun Zhu, Junichi Suzuki and Pruet Boonma Scheduling of Construction Projects with a Hybrid Evolutionary Algorithm’s Application 295 Wojciech Bożejko, Zdzisław Hejducki, Magdalena Rogalska and Mieczysław Wodecki A Memetic Algorithm for the Car Renter Salesman Problem 309 Marco Goldbarg, Paulo Asconavieta and Elizabeth Goldbarg Multi-Objective Scheduling on a Single Machine with Evolutionary Algorithm 327 A. S. Xanthopoulos, D. E. Koulouriotis and V. D. Tourassis Evolutionary Algorithms in Decomposition-Based Logic Synthesis 343 Mariusz Rawski A Memory-Storable Quantum-Inspired Evolutionary Algorithm for Network Coding Resource Minimization 363 Yuefeng Ji and Huanlai Xing Using Evolutionary Algorithms for Optimization of Analogue Electronic Filters 381 Lukáš Dolívka and Jiří Hospodka Chapter 10 Chapter 11 Chapter 12 Chapter 13 Part 2 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 Contents VII Evolutionary Optimization of Microwave Filters 407 Maria J. P. Dantas, Adson S. Rocha, Ciro Macedo, Leonardo da C. Brito, Paulo C. M. Machado and Paulo H. P. de Carvalho Feature Extraction from High-Resolution Remotely Sensed Imagery using Evolutionary Computation 423 Henrique Momm and Greg Easson Evolutionary Feature Subset Selection for Pattern Recognition Applications 443 G.A. Papakostas, D.E. Koulouriotis, A.S. Polydoros and V.D. Tourassis A Spot Modeling Evolutionary Algorithm for Segmenting Microarray Images 459 Eleni Zacharia and Dimitris Maroulis Discretization of a Random Field – a Multiobjective Algorithm Approach 481 Guang-Yih Sheu Evolutionary Algorithms in Modelling of Biosystems 495 Rosario Guzman-Cruz, Rodrigo Castañeda-Miranda, Juan García- Escalante, Luis Solis-Sanchez, Daniel Alaniz-Lumbreras, Joshua Mendoza-Jasso, Alfredo Lara-Herrera, Gerardo Ornelas-Vargas, Efrén Gonzalez-Ramirez and Ricardo Montoya-Zamora Stages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role 521 Alina Sîrbu, Heather J. Ruskin and Martin Crane Evolutionary Algorithms in Crystal Structure Analysis 547 Attilio Immirzi, Consiglia Tedesco and Loredana Erra Evolutionary Enhanced Level Set Method for Structural Topology Optimization 565 Haipeng Jia, Chundong Jiang, Lihui Du, Bo Liu and Chunbo Jiang Chapter 21 Chapter 22 Chapter 23 Chapter 24 Chapter 25 Chapter 26 Chapter 27 Chapter 28 Chapter 29 Pref ac e Evolutionary algorithms (EAs) are the population-based metaheuristic optimization algorithms. Candidate solutions to the optimization problem are defi ned as individu- als in a population, and evolution of the population leads to fi nding be er solutions. The fi tness of individuals to the environment is estimated and some mechanisms in- spired by biological evolution are applied to evolution of the population. Genetic algorithm (GA), Evolution strategy (ES), Genetic programming (GP), and Evo- lutionary programming (EP) are very popular Evolutionary algorithms. Genetic Algo- rithm, which was presented by Holland in 1970s, mainly uses selection, crossover and mutation operators for evolution of the population. Evolutionary Strategy, which was presented by Rechenberg and Schwefel in 1960s, uses natural problem-dependent rep- resentations and primarily mutation and selection as operators. Genetic programming and Evolutionary programming are GA- and ES-based methodologies to fi nd com- puter program or mathematical function that perform user-defi ned task, respectively. As related techniques, Ant colony optimization (ACO) and Particle swarm optimiza- tion (PSO) are well known. Ant colony optimization (ACO) was presented by Dorigo in 1992 and Particle swarm optimization (PSO) was by Kennedy, Eberhart and Shi in 1995. While Genetic Algorithm and Evolutionary Strategy are inspired from the geneti- cal evolution, Ant colony optimization and Particle swarm optimization are from the behavior of social insects (ants) and bird swarm, respectively. Therefore, Ant colony optimization and Particle swarm optimization are usually classifi ed into the swarm intelligence algorithms. Evolutionary algorithms are successively applied to wide optimization problems in the engineering, marketing, operations research, and social science, such as include scheduling, genetics, material selection, structural design and so on. Apart from math- ematical optimization problems, evolutionary algorithms have also been used as an experimental framework within biological evolution and natural selection in the fi eld of artifi cial life. The book consists of 29 chapters. Chapters 1 to 9 describe the algorithms for enhancing the search performance of evolutionary algorithms such as Genetic Algorithm, Swarm Optimization Algorithm and Quantum-inspired Algorithm. Chapter 10 introduces the programming language for evolutionary algorithm. Chapter 11 explains evolutionary algorithms for the fuzzy data problems. Chapters 12 to 13 discuss theoretical analysis of evolutionary algorithms. The remaining chapters describe the applications of the X Preface evolutionary algorithms. In chapters 12 to 17, the evolutionary algorithms are applied to several scheduling problems such as Traveling salesman problem, Job Scheduling problem and so on. Chapters 18 and 24 describe how to use evolutionary algorithm to logic synthesis, network coding, fi lters, pa ern recognition and so on. Chapters 25 to 29 also discuss the other applications of evolutionary algorithms such as random fi eld discretization, biosystem simulation, gene regulatory, crystal structure analysis and structural design. Eisuke Kita Graduate School of Information Science Nagoya University Japan [...]... knowledge into evolutionary algorithms Fig 2 Hybridization of Evolutionary Algorithms In Fig 2 some possibilities to hybridize evolutionary algorithms are illustrated At first, the initial population can be generated by incorporating solutions of existing algorithms or by using heuristics, local search, etc In addition, the local search can be applied to the population of offsprings Actually, the evolutionary. .. heuristics Usually, evolutionary algorithms are used for problem solving, where a lot of experience and knowledge is accumulated in various heuristic algorithms Typically, these algorithms work well on limited number of problems (Hoos & Stützle, 2005) On the other hand, evolutionary algorithms are a general method suitable to solve very different kinds of problems In general, these algorithms are less... Part 1 New Algorithms 1 Hybridization of Evolutionary Algorithms Iztok Fister, Marjan Mernik and Janez Brest University of Maribor Slovenia 1 Introduction Evolutionary algorithms are a type of general problem solvers that can be applied to many difficult optimization problems Because of their generality, these algorithms act similarly like Swiss Army knife (Michalewicz... general concept of self-adaptation as well (Meyer-Nieberg & Beyer, 2007) 3 How to hybridize the self-adaptive evolutionary algorithms Evolutionary algorithms are a generic tool that can be used for solving many hard optimization problems However, the solving of that problems showed that evolutionary algorithms are too problem-independent Therefore, there are hybridized with several techniques and heuristics... hybridization between two evolutionary algorithms (Grefenstette, 1986), • neural network assisted evolutionary algorithm (Wang, 2005), • fuzzy logic assisted evolutionary algorithm (Herrera & Lozano, 1996; Lee & Takagi, 1993), • particle swarm optimization assisted evolutionary algorithm (Eberhart & Kennedy, 1995; Kennedy & Eberhart, 1995), • ant colony optimization assisted evolutionary algorithm (Fleurent... solutions becomes fitter and fitter Finally, the evolutionary search can be iterated until a solution with sufficient quality (fitness) is found or the predefined number of generations is reached (Eiben & Smith, 2003) Note that some steps in Fig 1 can be omitted (e.g., mutation, survivor selection) 4 Evolutionary Algorithms Fig 1 Scheme of Evolutionary Algorithms An evolutionary search is categorized by two... aim of this operator is to directs the evolutionary search into new undiscovered regions of the search space, while on the other hand exploits problem specific knowledge To avoid wrong setting of parameters that control the behavior of the evolutionary algorithm, the self-adaptation is used as well Such 24 Evolutionary Algorithms hybrid self-adaptive evolutionary algorithms have been applied to the the... Swiss Army knife is fine Similarly, when a problem to be solved from a domain where the problem-specific knowledge is absent evolutionary algorithms can be successfully applied Evolutionary algorithms are easy to implement and often provide adequate solutions An origin of these algorithms is found in the Darwian principles of natural selection (Darwin, 1859) In accordance with these principles, only... to direct the evolutionary search to new, undiscovered regions of search space In fact, the neutral survivor selection represents hybridization of evolutionary operators, in this case, the survivor selection operator The hybrid self-adaptive evolutionary algorithm can be used especially for solving of the hardest combinatorial optimization problems (Fister et al., 2010) 6 Evolutionary Algorithms The... with higher fitness, the valleys points with the lower fitness while plateaus denotes regions, 10 Evolutionary Algorithms where the solutions are neutral (Stadler, 1995) The concept of the fitness landscape plays an important role in evolutionary computation as well Moreover, with its help behavior of evolutionary algorithms by solving the optimization problem can be understood If on the search space we look . problem specific knowledge into evolutionary algorithms. Fig. 2. Hybridization of Evolutionary Algorithms In Fig. 2 some possibilities to hybridize evolutionary algorithms are illustrated. At. University Japan Part 1 New Algorithms Hybridization of Evolutionary Algorithms Iztok Fister, Marjan Mernik and Janez Brest University of Maribor Slovenia 1. Introduction Evolutionary algorithms are a. knowledge is absent evolutionary algorithms can be successfully applied. Evolutionary algorithms are easy to implement and often provide adequate solutions. An origin of these algorithms is found