REAL-WORLD APPLICATIONS OF GENETIC ALGORITHMS Edited by Olympia Roeva Real-World Applications of Genetic Algorithms Edited by Olympia Roeva Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. 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. As for readers, this license allows users to download, copy and build upon published chapters even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. Notice 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 chapters. 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 Marina Jozipovic Technical Editor Teodora Smiljanic Cover Designer InTech Design Team First published February, 2012 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Real-World Applications of Genetic Algorithms, Edited by Olympia Roeva p. cm. ISBN 978-953-51-0146-8 Contents Preface IX Chapter 1 Different Tools on Multi-Objective Optimization of a Hybrid Artificial Neural Network – Genetic Algorithm for Plasma Chemical Reactor Modelling 1 Nor Aishah Saidina Amin and I. Istadi Chapter 2 Application of Bio-Inspired Algorithms and Neural Networks for Optimal Design of Fractal Frequency Selective Surfaces 27 Paulo Henrique da Fonseca Silva, Marcelo Ribeiro da Silva, Clarissa de Lucena Nóbrega and Adaildo Gomes D’Assunção Chapter 3 Evolutionary Multi-Objective Algorithms 53 Aurora Torres, Dolores Torres, Sergio Enriquez, Eunice Ponce de León and Elva Díaz Chapter 4 Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems 81 Elias D. Niño Chapter 5 Evolutionary Techniques in Multi-Objective Optimization Problems in Non-Standardized Production Processes 109 Mariano Frutos, Ana C. Olivera and Fernando Tohmé Chapter 6 A Hybrid Parallel Genetic Algorithm for Reliability Optimization 127 Ki Tae Kim and Geonwook Jeon Chapter 7 Hybrid Genetic Algorithm-Support Vector Machine Technique for Power Tracing in Deregulated Power Systems 147 Mohd Wazir Mustafa, Mohd Herwan Sulaiman, Saifulnizam Abd. Khalid and Hussain Shareef VI Contents Chapter 8 Hybrid Genetic Algorithm for Fast Electromagnetic Synthesis 165 Artem V. Boriskin and Ronan Sauleau Chapter 9 A Hybrid Methodology Approach for Container Loading Problem Using Genetic Algorithm to Maximize the Weight Distribution of Cargo 183 Luiz Jonatã Pires de Araújo and Plácido Rogério Pinheiro Chapter 10 Hybrid Genetic Algorithms for the Single Machine Scheduling Problem with Sequence-Dependent Setup Times 199 Aymen Sioud, MarcGravel and Caroline Gagné Chapter 11 Genetic Algorithms and Group Method of Data Handling- Type Neural Networks Applications in Poultry Science 219 Majid Mottaghitalb Chapter 12 New Approaches to Designing Genes by Evolution in the Computer 235 Alexander V. Spirov and David M. Holloway Chapter 13 Application of Genetic Algorithms and Ant Colony Optimization for Modelling of E. coli Cultivation Process 261 Olympia Roeva and Stefka Fidanova Chapter 14 Multi-Objective Genetic Algorithm to Automatically Estimating the Input Parameters of Formant-Based Speech Synthesizers 283 Fabíola Araújo, Jonathas Trindade, José Borges, Aldebaro Klautau and Igor Couto Chapter 15 Solving Timetable Problem by Genetic Algorithm and Heuristic Search Case Study: Universitas Pelita Harapan Timetable 303 Samuel Lukas, Arnold Aribowo and Milyandreana Muchri Chapter 16 Genetic Algorithms for Semi-Static Wavelength-Routed Optical Networks 317 R.J. Durán, I. de Miguel, N. Merayo, P. Fernández, J.C. Aguado, A. Bahillo, R. de la Rosa and A. Alonso Chapter 17 Surrogate-Based Optimization 343 Zhong-Hua Han and Ke-Shi Zhang Preface Genetic Algorithms are a part of Evolutionary Computing, which is a rapidly growing area of Artificial Intelligence. The popularity of Genetic Algorithms is reflected in the increasing amount of literature devoted to theoretical works and real-world applications in both scientific and engineering areas. The useful application and the proper combination of the different Genetic Algorithms with the various optimization algorithms is still an open research topic. This book addresses some of the most recent issues, with the theoretical and methodological aspects, of evolutionary multi-objective optimization problems and the various design challenges using different hybrid intelligent approaches. Multi- objective optimization has been available for about two decades, and its application in real-world problems is continuously increasing. Furthermore, many applications function more effectively using a hybrid systems approach. Hybridization of Genetic Algorithms is getting popular due to their capabilities in handling different problems involving complexity, noisy environment, uncertainty, etc. The book presents hybrid techniques based on Artificial Neural Network, Fuzzy Sets, Automata Theory, other metaheuristic or classical algorithms, etc. The volume examines various examples of algorithms in different real-world application domains as graph growing problem, speech synthesis, traveling salesman problem, scheduling problems, antenna design, genes design, modeling of chemical and biochemical processes etc. The book, organized in 17 chapters, begins with several applications of Hybrid Genetic Algorithms in wide range of problems. Further, some applications of Genetic Algorithms and other heuristic search methods are presented. The objective of Chapter 1 is to model and to optimize the process performances simultaneously in the plasma-catalytic conversion of methane such that the optimal process performances are obtained at the given process parameters. A Hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) is successfully developed to model, to simulate and to optimize simultaneously a catalytic-dielectric-barrier discharge plasma reactor. The integrated ANN-GA method facilitates powerful modeling and multi-objectives optimization for co-generation of synthesis gas, C 2 and higher hydrocarbons from methane and carbon dioxide in a dielectric barrier discharge plasma reactor. X Preface Chapter 2 presents a new fast and accurate electromagnetic optimization technique combining full-wave method of moments, bio-inspired algorithms, continuous Genetic Algorithm and Particle Swarm Optimization, and multilayer perceptrons Artificial Neural Networks. The proposed optimization technique is applied for optimal design of frequency selective surfaces with fractal patch elements. A fixed frequency selective surface screen geometry is chosen a priori and then a smaller subset of frequency selective surface design variables is optimized to achieve a desired bandstop filter specification. The main contribution of the Chapter 3 is the test of the Hybrid MOEA-HCEDA Algorithm and the quality index based on the Pareto front used in the graph drawing problem. The Pareto front quality index printed on each generation of the algorithm showed a convergent curve. The results of the experiments show that the algorithm converges. A graphical user interface is constructed providing users with a tool for a friendly and easy to use graphs display. The automatic drawing of optimized graphs makes it easier for the user to compare results appearing in separate windows, giving the user the opportunity to choose the graph design which best suits their needs. Chapter 4 studies metaheuristics based on the Automata Theory for the multi-objective optimization of combinatorial problems. The SAMODS (Simulated Annealing inspired Algorithm), SAGAMODS (Evolutionary inspired Algorithm) and EMODS (using Tabu Search) algorithms are presented. Presented experimental results of each proposed algorithm using multi-objective metrics from the specialized literature show that the EMODS has the best performance. In some cases the behavior of SAMODS and SAGAMODS tend to be the same – similar error rate. Chapter 5 presents a Hybrid Genetic Algorithm (Genetic Algorithm linked to a Simulated Annealing) intended to solve the Flexible Job-Shop Scheduling Problem procedure able to schedule the production in a Job-Shop manufacturing system. The authors show that this Hybrid Genetic Algorithm yields more solutions in the Approximate Pareto Frontier than other algorithms. A platform and programming language independent interface for search algorithms has been used as a guide for the implementation of the proposed hybrid algorithm. Chapter 6 suggests mathematical programming models and a Hybrid Parallel Genetic Algorithm (HPGA) for reliability optimization with resource constraints. The considered algorithm includes different heuristics such as swap, 2-opt, and interchange for an improvement solution. The experimental results of HPGA are compared with the results of existing meta-heuristics. The suggested algorithm presents superior solutions to all problems and found that the performance is superior to existing meta-heuristics. Chapter 7 discusses the effectiveness of Genetic Algorithms in determining the optimal values of hyper-parameters of Least Squares-Support Vector Machines to solve power tracing problem. The developed hybrid Genetic Algorithm-Support Vector Machines (GA- [...]... reasoning behind selection of the Genetic Algorithms control parameters, discuss the ways of boosting the algorithm efficiency, and finally introduce a simple Global-local Hybrid Genetic Algorithms capable of fast and reliable optimization of multi-parameter and multi-extremum functions The effectiveness of the proposed algorithm is demonstrated by numerical examples, namely: synthesis of linear antenna arrays... Method of Data Handling-type Neural Network with Genetic Algorithm used to develop the early egg production in broiler breeder By means of the Group Method of Data Handling Algorithm, a model can be represented as a set of quadratic polynomials Genetic Algorithms are deployed to assign the number of neurons (polynomial equations) in the network and to find the optimal set of appropriate coefficients of. .. of α for star point with respect to design depends on the number of 4 Real-World Applications of Genetic Algorithms points in the factorial portion of the design which is given in Equation (1) (Montgomery, 2001; Clarke & Kempson, 1997) α = ( nc )1/4 (1) where nc is number of points in the cube portion of the design (nc = 2k, k is number of factors) Since there are four parameters/factors in this experiment,... Multi-Objective Optimization of a Hybrid Artificial Neural Network – Genetic Algorithm for Plasma Chemical Reactor Modelling 21 Fig 11 Effect of total feed flowrate (X3) and CH4/CO2 ratio (X1) toward C2 hydrocarbons yield (y4) Fig 12 Effect of total feed flowrate (X3) and discharge voltage (X2) toward C2 hydrocarbons yield (y4) 22 Real-World Applications of Genetic Algorithms Fig 13 Effect of reactor temperature... retroGenetic Algorithm in comparison with the standard Genetic Algorithm Chapter 13 examines the use of Genetic Algorithms and Ant Colony Optimization for parameter identification of a system of nonlinear differential equations modeling the fed-batch cultivation process of the bacteria E coli The results from both XI XII Preface metaheuristics Genetic Algorithms and Ant Colony Optimization are compared... the architecture design of the system and show some experiments implementing the system The objective of Chapter 16 is to show a set of single-objective and multi-objective Genetic Algorithms, designed by the Optical Communications Group at the University of Valladolid, to optimize the performance of semi-static Wavelength-Routed Optical Networks (WRONs) The fundamentals of those algorithms, i.e., the... Hybrid Genetic Algorithms to solve the sequence-dependent setup times single machine problem The proposed approaches are essentially based on adapting highly specialized genetic operators to the specificities of the studied problem The numerical experiments demonstrate the efficiency of the hybrid algorithms for this problem A natural conclusion from these experimental results is that Genetic Algorithms. .. objective based on two steps: (a) development of an ANN-based process model which has inputs of process operating parameters of plasma – catalytic reactor, and output(s) of process output/response variable(s), i.e yield of C2hydrocarbons or hydrogen, or methane conversion; and (b) development of GA technique for multi-objective optimization of the ANN model Input space of the ANN model is optimized using the... problem based on the principle of survival of the fittest during the evolution The GA implements the “survival of the fittest” and genetic propagation of characteristics” principles of biological evolution for searching the solution space of an optimization problem In nature, individuals must adapt to the frequent changing environment in order to survive The GA is one of the strategic randomized search... the parents for the next generation The range of the scaled values influences performance of the GA If the scaled values vary too widely, the individuals with the highest scaled values reproduce too rapidly, taking over the 10 Real-World Applications of Genetic Algorithms population gene pool too quickly, and preventing the GA from searching other areas of the solution space On the other hand, if the . REAL-WORLD APPLICATIONS OF GENETIC ALGORITHMS Edited by Olympia Roeva Real-World Applications of Genetic Algorithms Edited by Olympia. central point. The value of α for star point with respect to design depends on the number of Real-World Applications of Genetic Algorithms 4 points in the factorial portion of the design which. modeling of chemical and biochemical processes etc. The book, organized in 17 chapters, begins with several applications of Hybrid Genetic Algorithms in wide range of problems. Further, some applications