Real WorldApplns. of Genetic Algorithms [appl. math]

376 Pages·2012·12.6 MB·English

by  O. Roeva

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
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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 [email protected]

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, C2 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-