







GENETIC
ALGORITHMS AND EVOLUTIONARY COMPUTATION
David
E. Goldberg, consulting editor
Researchers and practitioners alike are increasingly turning
to search, optimization, and machinelearning procedures based
on natural selection and genetics to solve problems across
the spectrum of human endeavor. These genetic algorithms and
techniques of evolutionary computation are solving problems
and inventing new hardware and software that rival human designs.
Genetic Algorithms and Evolutionary Computation will
publish research monographs, edited collections, and graduatelevel
texts in this rapidly growing field. Primary areas of coverage
include the theory, implementation, and application of genetic
algorithms (GAs), evolution strategies (ESs), evolutionary
programming (EP), learning classifier systems (LCSs) and other
variants of genetic and evolutionary computation (GEC). Proposals
in related fields such as artificial life, adaptive behavior,
artificial immune systems, agentbased systems, neural computing,
fuzzy systems, and quantum computing will be considered for
publication in this series as long as GEC techniques are part
of or inspiration for the system being described. Manuscripts
describing GEC applications in all areas of engineering, commerce,
the sciences, and the humanities are encouraged.




CantuPaz,
Erick
Efficient
and Accurate Parallel Genetic Algorithms
Genetic Algorithms and Evolutionary Computation, Volume 1.
2000. Kluwer Academic Pub; ISBN: 0792372212 

Book Description
As
genetic algorithms (GAs) become increasingly popular, they
are applied to difficult problems that may require considerable
computations. In such cases, parallel implementations of GAs
become necessary to reach highquality solutions in reasonable
times. But, even though their mechanics are simple, parallel
GAs are complex nonlinear algorithms that are controlled
by many parameters, which are not well understood. Efficient
and Accurate Parallel Genetic Algorithms is about the design
of parallel GAs. It presents theoretical developments that
improve our understanding of the effect of the algorithm's
parameters on its search for quality and efficiency. These
developments are used to formulate guidelines on how to choose
the parameter values that minimize the execution time while
consistently reaching solutions of high quality. Efficient
and Accurate Parallel Genetic Algorithms can be read in several
ways, depending on the readers' interests and their previous
knowledge about these algorithms. Newcomers to the field will
find the background material in each chapter useful to become
acquainted with previous work, and to understand the problems
that must be faced to design efficient and reliable algorithms.
Potential users of parallel GAs that may have doubts about
their practicality or reliability may be more confident after
reading this book and understanding the algorithms better.
Those who are ready to try a parallel GA on their applications
may choose to skim through the background material, and use
the results directly without following the derivations in
detail. These readers will find that using the results can
help them to choose the type of parallel GA that best suits
their needs, without having to invest the time to implement
and test various options. Once that is settled, even the most
experienced users dread the long and frustrating experience
of configuring their algorithms by trial and error. The guidelines
contained herein will shorten dramatically the time spent
tweaking the algorithm, although some experimentation may
still be needed for finetuning. Efficient and Accurate Parallel
Genetic Algorithms is suitable as a secondary text for a graduate
level course, and as a reference for researchers and practitioners
in industry.






Book
Description
Estimation of Distribution Algorithms: A New Tool for Evolutionary
Computation is devoted to a new paradigm for evolutionary
computation, named estimation of distribution algorithms (EDAs).
This new class of algorithms generalizes genetic algorithms
by replacing the crossover and mutation operators with learning
and sampling from the probability distribution of the best
individuals of the population at each iteration of the algorithm.
Working in such a way, the relationships between the variables
involved in the problem domain are explicitly and effectively
captured and exploited. This text constitutes the first compilation
and review of the techniques and applications of this new
tool for performing evolutionary computation. Estimation of
Distribution Algorithms: A New Tool for Evolutionary Computation
is clearly divided into three parts. Part I is dedicated to
the foundations of EDAs. In this part, after introducing some
probabilistic graphical models  Bayesian and Gaussian networks
 a review of existing EDA approaches is presented, as well
as some new methods based on more flexible probabilistic graphical
models. A mathematical modeling of discrete EDAs is also presented.
Part II covers several applications of EDAs in some classical
optimization problems: the travelling salesman problem, the
job scheduling problem, and the knapsack problem. EDAs are
also applied to the optimization of some wellknown combinatorial
and continuous functions. Part III presents the application
of EDAs to solve some problems that arise in the machine learning
field: feature subset selection, feature weighting in KNN
classifiers, rule induction, partial abductive inference in
Bayesian networks, partitional clustering, and the search
for optimal weights in artificial neural networks. Estimation
of Distribution Algorithms: A New Tool for Evolutionary Computation
is a useful and interesting tool for researchers working in
the field of evolutionary computation and for engineers who
face realworld optimization problems. This book may also
be used by graduate students and researchers in computer science.




Jürgen Branke
Evolutionary
Optimization in Dynamic Environments
Genetic Algorithms and Evolutionary Computation, Volume 3.
2001. Kluwer Academic Pub; ISBN: 0792376315 

Book
Description
Evolutionary Algorithms (EAs) have grown into a mature field
of research in optimization, and have proven to be effective
and robust problem solvers for a broad range of static realworld
optimization problems. Yet, since they are based on the principles
of natural evolution, and since natural evolution is a dynamic
process in a changing environment, EAs are also well suited
to dynamic optimization problems. Evolutionary Optimization
in Dynamic Environments is the first comprehensive work on
the application of EAs to dynamic optimization problems. It
provides an extensive survey on research in the area and shows
how EAs can be successfully used to + continuously and efficiently
adapt a solution to a changing environment, + find a good
tradeoff between solution quality and adaptation cost, +
find robust solutions whose quality is insensitive to changes
in the environment, + find flexible solutions which are not
only good but that can be easily adapted when necessary. All
four aspects are treated in this book, providing a holistic
view on the challenges and opportunities when applying EAs
to dynamic optimization problems. The comprehensive and uptodate
coverage of the subject, together with details of latest original
research, makes Evolutionary Optimization in Dynamic Environments
an invaluable resource for researchers and professionals who
are dealing with dynamic and stochastic optimization problems,
and who are interested in applying local search heuristics,
such as evolutionary algorithms. 



Butz,
Martin
Anticipatory
Learning Classifier Systems
Genetic Algorithms and Evolutionary Computation, Volume
4. 2001. Kluwer
Academic Pub; ISBN:0792376307


Book
Description
Anticipatory Learning Classifier Systems describes
the state of the art of anticipatory learning classifier systemsadaptive
rule learning systems that autonomously build anticipatory
environmental models. An anticipatory model specifies all
possible actioneffects in an environment with respect to
given situations. It can be used to simulate anticipatory
adaptive behavior. Anticipatory Learning Classifier Systems
highlights how anticipations influence cognitive systems and
illustrates the use of anticipations for (1) faster reactivity,
(2) adaptive behavior beyond reinforcement learning, (3) attentional
mechanisms, (4) simulation of other agents and (5) the implementation
of a motivational module. The book focuses on a particular
evolutionary model learning mechanism, a combination of a
directed specializing mechanism and a genetic generalizing
mechanism. Experiments show that anticipatory adaptive behavior
can be simulated by exploiting the evolving anticipatory model
for even faster model learning, planning applications, and
adaptive behavior beyond reinforcement learning. Anticipatory
Learning Classifier Systems gives a detailed algorithmic description
as well as a program documentation of a C++ implementation
of the system. It is an excellent reference for researchers
interested in adaptive behavior and machine learning from
a cognitive science perspective as well as those who are interested
in combining evolutionary learning mechanisms for learning
and optimization tasks.


Knjazew,
Dimitri
Omega : A Competent Genetic Algorithm for Solving Permutation
and Scheduling Problems
Genetic Algorithms and Evolutionary Computation, Volume
6. 2001.
Kluwer Academic Pub; ISBN:0792374606


Book Description
OmeGA: A Competent Genetic Algorithm for Solving Permutation
and Scheduling Problems addresses two increasingly important
areas in GA implementation and practice. OmeGA, or the ordering
messy genetic algorithm, combines some of the latest in competent
GA technology to solve scheduling and other permutation problems.
Competent GAs are those designed for principled solutions
of hard problems, quickly, reliably, and accurately. Permutation
and scheduling problems are difficult combinatorial optimization
problems with commercial import across a variety of industries.
This book approaches both subjects systematically and clearly.
The first part of the book presents the clearest description
of messy GAs written to date along with an innovative adaptation
of the method to ordering problems. The second part of the
book investigates the algorithm on boundedly difficult test
functions, showing principled scale up as problems become
harder and longer. Finally, the book applies the algorithm
to a test function drawn from the literature of scheduling.






