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Evolutionary algorithms (EAs), as well as other bio-inspired
heuristics, are widely usedto
solvenumericaloptimizationproblems.However, intheir or- inal
versions, they are limited to unconstrained search spaces i.e they
do not include a mechanism to incorporate feasibility information
into the ?tness function. On the other hand, real-world problems
usually have constraints in their models. Therefore, a considerable
amount of research has been d- icated to design and implement
constraint-handling techniques. The use of (exterior) penalty
functions is one of the most popular methods to deal with
constrained search spaces when using EAs. However, other
alternative me- ods have been proposed such as: special encodings
and operators, decoders, the use of multiobjective concepts, among
others. An e?cient and adequate constraint-handling technique is a
key element in the design of competitive evolutionary algorithms to
solve complex op- mization problems. In this way, this subject
deserves special research e?orts. After
asuccessfulspecialsessiononconstraint-handlingtechniquesusedin
evolutionary algorithms within the Congress on Evolutionary
Computation (CEC) in 2007, and motivated by the kind invitation
made by Dr. Janusz Kacprzyk, I decided to edit a book, with the aim
of putting together recent studies on constrained numerical
optimization using evolutionary algorithms and other bio-inspired
approaches. The intended audience for this book comprises graduate
students, prac-
tionersandresearchersinterestedonalternativetechniquestosolvenumerical
optimization problems in presence of constraints
Evolutionary algorithms (EAs), as well as other bio-inspired
heuristics, are widely usedto
solvenumericaloptimizationproblems.However, intheir or- inal
versions, they are limited to unconstrained search spaces i.e they
do not include a mechanism to incorporate feasibility information
into the ?tness function. On the other hand, real-world problems
usually have constraints in their models. Therefore, a considerable
amount of research has been d- icated to design and implement
constraint-handling techniques. The use of (exterior) penalty
functions is one of the most popular methods to deal with
constrained search spaces when using EAs. However, other
alternative me- ods have been proposed such as: special encodings
and operators, decoders, the use of multiobjective concepts, among
others. An e?cient and adequate constraint-handling technique is a
key element in the design of competitive evolutionary algorithms to
solve complex op- mization problems. In this way, this subject
deserves special research e?orts. After
asuccessfulspecialsessiononconstraint-handlingtechniquesusedin
evolutionary algorithms within the Congress on Evolutionary
Computation (CEC) in 2007, and motivated by the kind invitation
made by Dr. Janusz Kacprzyk, I decided to edit a book, with the aim
of putting together recent studies on constrained numerical
optimization using evolutionary algorithms and other bio-inspired
approaches. The intended audience for this book comprises graduate
students, prac-
tionersandresearchersinterestedonalternativetechniquestosolvenumerical
optimization problems in presence of constraints
This book aims to discuss the core and underlying principles and
analysis of the different constraint handling approaches. The main
emphasis of the book is on providing an enriched literature on
mathematical modelling of the test as well as real-world problems
with constraints, and further development of generalized constraint
handling techniques. These techniques may be incorporated in
suitable metaheuristics providing a solid optimized solution to the
problems and applications being addressed. The book comprises
original contributions with an aim to develop and discuss
generalized constraint handling approaches/techniques for the
metaheuristics and/or the applications being addressed. A variety
of novel as well as modified and hybridized techniques have been
discussed in the book. The conceptual as well as the mathematical
level in all the chapters is well within the grasp of the
scientists as well as the undergraduate and graduate students from
the engineering and computer science streams. The reader is
encouraged to have basic knowledge of probability and mathematical
analysis and optimization. The book also provides critical review
of the contemporary constraint handling approaches. The
contributions of the book may further help to explore new avenues
leading towards multidisciplinary research discussions. This book
is a complete reference for engineers, scientists, and students
studying/working in the optimization, artificial intelligence (AI),
or computational intelligence arena.
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