Variants of Evolutionary Algorithms for Real-World Applications [Chiong, Weise & Michalewicz 2011-11-05].pdf

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Variants of Evolutionary Algorithms for Real-World
Applications
Raymond Chiong, Thomas Weise,
and Zbigniew Michalewicz (Eds.)
Variants of Evolutionary
Algorithms for Real-World
Applications
ABC
Editors
Raymond Chiong
Faculty of ICT
Swinburne University of Technology
Melbourne, VIC 3122, Australia
E-mail: rchiong@swin.edu.au
Thomas Weise
Nature Inspired Computation and
Applications Laboratory
School of Computer Science and
Technology
University of Science and
Technology of China (USTC)
Hefei 230027, Anhui, China
E-mail: tweise@ustc.edu.cn
Zbigniew Michalewicz
School of Computer Science
University of Adelaide
Adelaide, SA 5005, Australia
E-mail: zbyszek@cs.adelaide.edu.au
ISBN 978-3-642-23423-1
DOI 10.1007/978-3-642-23424-8
e-ISBN 978-3-642-23424-8
Library of Congress Control Number: 2011935740
c 2012 Springer-Verlag Berlin Heidelberg
This work is subject to copyright. All rights are reserved, whether the whole or part of the mate-
rial is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Dupli-
cation of this publication or parts thereof is permitted only under the provisions of the German
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be obtained from Springer. Violations are liable to prosecution under the German Copyright Law.
The use of general descriptive names, registered names, trademarks, etc. in this publication does
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Printed on acid-free paper
987654321
springer.com
Preface
Started as a mere academic curiosity, Evolutionary Algorithms (EAs) first
came into sight back in the 1960s. However, it was not until the 1980s that
the research on EAs became less theoretical and more practical. As a manifes-
tation of population-based, stochastic search algorithms that mimic natural
evolution, EAs use genetic operators such as crossover and mutation for the
search process to generate new solutions through a repeated application of
variation and selection.
Due to their ability to find excellent solutions for conventionally hard and
dynamic problems within acceptable time, EAs have attracted interest from
many researchers and practitioners in recent years. The general-purpose,
black-box character of EAs makes them suitable for a wide range of real-
world applications. Standard EAs such as Genetic Algorithms (GAs) and
Genetic Programming (GP) are becoming more and more accepted in the in-
dustry and commercial sectors. With the dramatic increase in computational
power today, an incredible diversification of new application areas of these
techniques can be observed. At the same time, variants and other classes of
evolutionary optimisation methods such as Differential Evolution, Estimation
of Distribution Algorithms, Co-evolutionary Algorithms and Multi-Objective
Evolutionary Algorithms (MOEAs) have been developed.
When applications or systems utilising EAs reach the production stage,
off-the-shelf versions of these methods are typically replaced by dedicated
algorithm variants. These specialised EAs often use tailored reproduction
operators, search spaces differing significantly from the well-known binary
or tree-based encodings, non-trivial genotype-phenotype mappings, or are
hybridised with other optimisation algorithms. This book aims to promote
the practitioner’s view on EAs by giving a comprehensive discussion of
how EAs can be adapted to the requirements of various applications in the
VI
Preface
real-world domains. It comprises 14 chapters, which can be categorised into
the following four sections:
•
•
•
•
Section
Section
Section
Section
I: Introduction
II: Planning & Scheduling
III: Engineering
IV: Data Collection, Retrieval & Mining
The first section contains only one single chapter – the introductory chap-
ter. In this chapter,
Blum et al.
re-visit the fundamental question of “what
is an EA?” in an attempt to clearly define the scope of this book. In this
regard, they systematically explore and discuss both the traditional and the
modern views on this question by relating it to other areas in the field. That
is, apart from discussing the main characteristics of conventional EAs they
also extend their discussion to Memetic Algorithms (MAs) and the Swarm In-
telligence algorithms. It appears that establishing semantic borders between
the different algorithm families is never easy, nor necessarily useful. In this
book, however, the focus will be on the traditional set of EAs like GAs, GP,
and their variants.
The second section of the book deals with planning and scheduling prob-
lems. Planning and scheduling activities are among the most important tasks
in Business and Industry. Once orders are placed by a customer, it is neces-
sary to schedule the purchase of raw materials and to decide which machines
are going to be used in order to create the ordered product in the desired qual-
ity. Often, multiple different client requests need to be facilitated at the same
time and the goal is to satisfy all of them in a timely and cost-effective man-
ner. However, it is not only the production steps that need to be scheduled.
In fact, the whole behaviour of a supply chain as well as the work assignments
for employees can be subject to planning. This section contains six chapters,
with different groups of researchers presenting efficient EA approaches to a
variety of real-world planning and scheduling problems.
The first chapter in this section by
Mohais et al.
introduces a tailor-made
EA for the process of bottling wine in a mass-production environment. Time-
varying (dynamic) constraints are the focus of this chapter. That is, schedul-
ing for job shop problems rarely starts with a blank sheet of paper. Instead,
some production processes will already be in progress. Hence, there is typ-
ically a set of scheduled operations that are fixed and cannot be modified
by optimisation, yet will influence the efficiency and feasibility of new plans.
Mohais et al. successfully approach the wine bottling problem with their
tailor-made evolutionary method.
Following which,
Toledo et al.
present a similar real-world problem for
soft-drink manufacturing plants known as the synchronised and integrated
two-level lot sizing and scheduling problem. Here, the first production level
has tanks storing the soft drink flavours and the second level corresponds
to the bottling lines. The problem involves capacity limits, different costs
and production times depending on the raw materials involved as well as the

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