Melanie Mitchell - An Introduction ...

Melanie Mitchell - An Introduction to Genetic Algorithms, stooges (hasło - stooges), ebooks, Consciousness Books ...

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Preface
This book introduces the
rapidly growing
field of
genetic algorithms
(
GAs
)
. Its
purpose
is to describe in
depth
some of the most
interesting
work in this field rather than to
attempt
a
complete
but
less detailed survey
. What is
"
most
interesting
"
is
,
of course
,
very subjective
;
the choice of
topics
reflects
my
own interests
,
which lean toward
machine
learning
,
scientific
modeling
,
and
"
artificial life
"
more than toward
optimization
and
engineering
. GAs have been
widely explored
in
.
all these areas
,
but
this
book concentratesmuch more on the former than on the latter. This
distin
-
guishes
it from other books on GAs
,
which focus
mainly
on
optimization
techniques
arid
engineering applications
.
In
technology
and scienceGAs have been used as
adaptive algorithms
for
solving practical problems
and as
computational
models of natural
evolutionary systems
. I will
give equal space
to thesetwo roles
,
and I will
also discuss their
complementary aspects
. In
describing
the
applications
and the
modeling projects
,
I will
venture
beyond
the strict boundaries
of
computer
science
into the worlds of
dynamical systems
theory
,
game
theory
,
molecular
biology
,
ecology
,
evolutionary biology
,
and
population
genetics
. Such
forays
are a
wonderful
perquisite
of GA research.
Just
as
GAs are
"
general
-
purpose
"
search methods
,
GA
researchershave to be
generalists
,
willing
to
step
out of their own
discipline
and
learn
something
about a new one in order to
pursue
a
promising application
or model .
Very
often this is done in collaboration between
scientists from different
disciplines
-
computer
scientists
working
with
biologists using
GAs
to
predict
the structure of
proteins
or with
political
scientists
modeling
the evolution of
cooperative
behavior
among
nations. Such collaborations
often lead to new
insights
in both fields . This
interdisciplinary
nature of
GA researchis one of the
qualities
that make it so valuable and so much
fun
.
The field of GAs and the broader field of
evolutionary computation
are
very young,
and most of the
important problems
remain
open
. I
hope
that this book will communicate some of the excitement and the
importance
of this
.
enterprise
,
and that it will
provide enough
information to
enable scientists in a
range
of
disciplines
to becomeinvolved .
The first
chapter
introduces
genetic algorithms
and their
terminology
,
sets the
stage by
describing
two
provocative
GA
applications
in detail
,
and
gives
a brief introduction to the
theory
of
GAs
.
Chapters
2 and 3 survey
some of the most
interesting
GA
applications
in
the fields of machine
learning
and scientific
modeling
.
Chapter
4
describesseveral
approach
es
to the
theory
of GAs
,
and
chapter
5 discuss es a number
of
important
implementation
issues that arise in the
application
of
GAs
.
The last
chapter
poses
some
currently
unanswered
questions
about
GAs and
surveys
some
prospects
for the future of
evolutionary computation
in
technology
and in
modeling
.
The
appendices
give
a a selected list of
general
references
on
GAs and instructions on how to obtain information
about GAs
on the
Internet
.
This book is for
anyone
with a
college
-level scientific
background
who
is interested in
learning
about or
using genetic
algorithms
. Some
knowledge
of
computer programming
is assumed. No mathematics
beyondal
-
gebra
is used
,
except
in
chapter
4
(
where calculus
,
vector
algebra
,
and
probability theory
come into
play
)
.
The book is meant to be accessible
to scientists in
any discipline
,
and
it could be used as a text for
graduate
or
upper
-
level
undergraduate
coursesin which GAs are featured. For
those who
get
hooked and want
to
explore
the field in more
depth
,
numerous
pointers
to the GA
literature are
given throughout
the text and in
the extensive
bibliography
. In addition
,
thought
exercisesand
computer
exerci
~
es are
given
at the end of each
chapter
.
Preface
viii
Acknowledgments
I am
very grateful
to the
following people
for
answering questions
,
for
commenting
on
chapters
,
and for
general
moral
support
while I was writing
this book
:
Dave
Ackley
,
Bob Axelrod
,
Mark Bedau
,
Rik Belew
,
Lashon
Booker
,
Ronda Butler
-
Villa
, Jim
Crutch field
,
Raja
Das
,
Doyne
Farmer
,
Marc Feldman
,
William Finnoff
,
Stephanie
Forrest
,
Bob French
,
Mark
Galassi
,
Howard Gutowitz
,
Doug
Hofstadter
, John
Holland
,
Greg
Huber
,
Jeff
lliara
,
David
Jefferson,
George
Johnson,
Terry
Jones,
Hiroaki Kitano
,
Jim
Levenick
,
Michael littman
,
Dan
McShea
,
Tom
Meyer
, Jack
Mitchell
,
Norma Mitchell
,
David Moser
,
Una
-
May
O
'
Reilly
,
Norman Packard
,
Richard Palmer
,
Rick Riolo
, Jonathan
Rough garden
,
Steffen Schulze-
Kremer
, Jonathan
Shapiro
,
Chuck
Taylor
,
Peter Todd
,
and Stewart WIlson
. I am also
grateful
to
Betty
Stanton
,
Harry
Stanton
,
Teri Mendelsohn
,
and Paul
Bethge
for their
help
and
patience
on
publishing
and editorial
matters. The Santa Fe Institute has
provided
an ideal environment for
my
researchand
writing
. I
especially
want to thank Mike Simmons and
Ginger
Richard son for academic
support
and advice at SF! and Deborah
Smith and
Joleen
Rocque
-Frank for secretarial
support
. I am
grateful
for
research
funding
from the Alfred P. Sloan Foundation
(grant
B1992- 46
)
,
the National ScienceFoundation
(
grant
1R
-
932020
)
,
and the
Department
of
Energy (grant
DE
-
FGO
-
94ER2523
)
.
This book stems from a series of lectures I
gave
at the 1992 Santa Fe
Institute
Complex Systems
Summer School.
Many
thanks to Dan Stein for
inviting
me to
give
theselectures
,
and
to the
1992
Summer
Schoolstudents
for their incisive
questions
and
comments
.
Finally
,
special
thanks to
John
Holland
for
introducing
me to
genetic
algorithms
in the
first
place
,
for
ongoing support
and
encouragement
of
my
work
,
and for
continuing
to
produce
and share ideas and
insights
about
complex adaptive systems
.
1
Genetic
Algorithms
: An Overview
Sciencearises from the
very
human desire to understand and control the
world . Over the course of
history
,
we humans have
gradually
built
up
a
grand
edifice of
knowledge
that enables us to
predict
,
to
varying
extents
,
the weather
,
the motions of the
planets
,
solar and lunar
eclipses
,
the courses of disease
,
the rise and fall of economic
growth
,
the
stages
of
language development
in children
,
and a vast
panorama
of other natural
,
social
,
and cultural
phenomena
. More
recently
we have even come to
understand some fundamental limits to our abilities to
predict
.
Over the
eonswe have
developed increasingly complex
means to control
many
aspects
of our lives and our interactions
with nature
,
and we have learned
,
often the hard
way
,
the extent to which other
aspects
are uncontrollable .
The advent of electronic
computers
has
arguably
been the most revolutionary
development
in the
history
of science
and
technology
. This
ongoing
revolution is
profoundly increasing
our
ability
to
predict
and control
nature in
ways
that were
barely
conceived of even half a
century ago
. For
many
,
the
crowning
achievementsof this revolution will be the creation
-
in
the form of
computer programs
- of new
species
of
intelligent
beings
,
and even of new forms of life .
The
goals
of
creating
artificial
intelligence
and artificial life can be
traced back to the
very beginnings
of the
computer age
. The earliest computer
scientists - Alan
Turing
,
John
von Neumann
,
Norbert
Wiener
,
and
others - were motivated in
large part by
visions of
imbuing computer
programs
with
intelligence
,
with the life-like
ability
to self
-
replicate
,
and
with the
adaptive capability
to learn and to control their
environments .
These
early pioneers
of
computer
science
were as much interested in biology
and
psychology
as in electronics
,
and
they
looked to natural
systems
as
guiding metaphors
for how to achieve
their visions . It should be no surprise
,
then
,
that from the earliest
daY
$
computers
were
applied
not
only
to
calculating
missile
trajectories
and
deciphering military
codesbut also
to
modeling
the brain
,
mimicking
human
learning
,
and
simulating
biological
evolution
.
These
biologically
motivated
computing
activities have
waxed and waned over
the
years
,
but since the
early
1980s
they
have
all
undergone
a
resurgence
in the
computation
research
community
. The
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