THE SUBJECT Excerpts
in Acrobat PDF format
Individuals and organizations are often faced with making tradeoffs
in order to achieve desirable outcomes. Choosing these tradeoffs
in the “best” way is the essence of the search and optimization
problem. Mathematical algorithms for search and optimization play
a large role in finding the best options in many problems in engineering,
business, medicine, and the physical and social sciences. Stochastic search and optimization pertains to problems where there is random
noise in the measurements provided to the algorithm and/or there
is injected (Monte Carlo) randomness in the algorithm itself. A countless
number of stochastic search and optimization problems arise in industry,
academia, and government.
SUMMARY OF THE BOOK
Introduction to Stochastic Search and Optimization provides a broad
survey of many of the most important methods in stochastic search
and optimization. These include random search, recursive least squares,
stochastic approximation, simulated annealing, evolutionary computation
(including genetic algorithms), and reinforcement learning. Also
included is a discussion of closely related subjects such as multiple
statistical comparisons, model selection, simulationbased optimization,
Markov chain Monte Carlo, and experimental design. These subjects
are covered in 17 chapters. Each chapter ends with some concluding
remarks, including comments on the historical perspective and on
the nexus with other topics in the book. Five appendices review essential
background information in multivariate analysis, matrix theory, statistical
testing, probability theory, pseudorandom number generation, and
Markov chains. All chapters and appendices include exercises. The
book concludes with an extensive list of references.
Broadly speaking, the first part of the book (Chapters 1–12) is devoted
to a description of core algorithms for stochastic search and optimization.
The
second part (Chapters 13–17) discusses some closely related subjects
in modeling, simulation, and estimation, including some important applications
of the algorithms
in Chapters 1–12.
PREREQUISITES AND INTENDED AUDIENCE
Readers should have a working knowledge of basic probability and
statistics, together with knowledge of multivariate calculus and
basic matrix algebra. Previous optimization experience is helpful,
but not required. With the exception of some of the material on probabilistic
convergence (Section C.2 in Appendix C), random number generation
(Appendix D), and possibly Markov processes (Appendix E), the appendices
are largely a review of the prerequisite subject matter.
The book may serve as either a reference book for researchers and practitioners
or as a textbook, the latter use being supported by exercises at the end of
every chapter and appendix. The relatively modest prerequisites are intended
to make the book broadly accessible, including to those who will actually use
the methods in practical settings.
PHILOSOPHY OF THE BOOK
As any author must do, I had to make many choices about overall
philosophy and topics to include or exclude. Aside from the inevitable
personal biases, my choices were guided by the need to keep the book
from reaching biblical proportions and the desire to make the book
accessible to industrial practitioners and master’s level students
in engineering and other areas.
This book focuses on methods that have a solid theoretical foundation and that
have a track record of effectiveness in a broad range of practical applications.
Without such restrictions, there are an almost limitless number of methods
that could be discussed (in particular, there are a huge number of highly specialized
and/or ad hoc methods discussed in the literature). Even with these restrictions,
there are a very large number of possible algorithms. Although the methods
here are among the most popular in the field, the coverage is not encyclopedic.
Rather than simply present various stochastic search and optimization algorithms
as a collection of distinct techniques, the book compares and contrasts the
algorithms within a broader context of stochastic methods. From this perspective,
the book demonstrates that relatively simple algorithms may perform as well
as—or better than—other “exotic” algorithms that may
have received a disproportionate amount of attention. A correct choice of algorithm
may lead to significant cost savings through the production of a better solution
to the problem and through savings in the effort required for implementation.
Because of the diverse areas in which this material applies, the book is not
directed toward any specific application area. Nevertheless, it does include
some applicationsoriented examples, but these are designed to be easily understandable
and not to require specialized knowledge. These simple examples are drawn from
areas such as control engineering, transportation, music, chemistry, economics,
medicine, and artificial intelligence. Further, the book includes an extensive
list of references, many of which provide detail on specific applications.
TRUTH IN ADVERTISING
There are perfect books and there are finished books. As such, allow
me to mention a few items and topics the book does not include. First,
as mentioned above, this book does not include detailed discussions
of many “serious” applications, although there are countless
realworld uses of the methods here. Rather, the focus is on generally
useful principles for a broad audience without devoting significant
attention to the inevitable applicationspecific details of a serious
implementation. Related to this, this book bucks the trend (particularly
among statistics books) of including analyses of numerous realworld
data sets. The aim is to encourage the reader to grasp basic principles
and not be distracted by the nuances and scale of many such data
sets. In an educational context, an instructor can introduce such
data as desired or have students pursue one or more of the many listed
references (and associated Web sites) that analyze such data.
A related distinction between this work and many others on algorithms is that
this book is not filled with a large number of Monte Carlo simulation (numerical)
studies. Certainly, the book includes some such studies, but the overall philosophy
is to emphasize approaches that have a solid theoretical foundation and to
present this foundation together with a limited number of simulation studies
that augment the theory. The rationale for such an approach is that a strong
reliance on numerical studies alone can provide misleading indications about
the effectiveness (or lack thereof) of an approach. All simulation studies
are problem specific, with results that may not transfer to other problems.
Further, it is difficult to provide a fully objective numerical comparison
when the algorithms must be “tuned” to the application. The temptation
exists to spend more time tuning favored algorithms than tuning other algorithms.
Although this book presents the theoretical basis of the algorithms, it does
not include lengthy proofs of such results. Rather, the reader is directed
to the literature for detailed derivations and proofs. (Many of the proofs
in the literature are quite intricate and use mathematics beyond the level
of this book.) Nevertheless, results are presented rigorously, with the aim
of having the reader gain an appreciation for the rationale and limits of various
algorithms.
There is no standard list of topics to be covered in a book on stochastic search
and optimization. Some experienced researchers and practitioners may find their
favorite algorithms excluded here. Please accept my apologies for such omissions.
Some of the specific technical topics not covered—which could well qualify
as stochastic search and optimization—are stochastic programming, dynamic
programming, Markov decision processes, and many of the optimization metaheuristics
(such as tabu search and ant colony optimization). Some of these are omitted
in the interest of providing balanced coverage of different general approaches
(gradientbased versus nongradientbased, single path versus populationbased,
etc.) while keeping the book between only two covers. Others are omitted because
there is little evidence of greater general efficiency or ease of use when
compared to some of the basic methods that are discussed in the book. Although
some may challenge the choice of topics, it is incontrovertible that the topics
included are important, are relevant to the book’s theme, and are widely
used in industrial and other practice.
Finally, this book does not review, endorse, or seriously discuss available
commercial software in stochastic search and optimization. (I am referring
to specialized software—e.g., a commercial package implementing a genetic
algorithm—rather than generic packages such as MATLAB or Microsoft EXCEL.)
Many “serious” implementations of the methods here rely on such
specialized software. An Internet search or perusal of the advertisements appearing
in many technical magazines or related publications will provide a number of
possible vendors.
