In
this
paper,
an
exact
algorithm
in
polynomial
time
is
developed
to
solve
unrestricted
binary
quadratic
programs.
The
computational
complexity
$O\left(
n^{\frac{15}{2}}\right)
$,
although
very
conservative,
it
sufficient
prove
that
minimization
problem
the
class
$P$.
implementation
aspects
are
also
described
detail
with
a
special
emphasis
on
transformation
of
program
into
linear
can
be
solved
time.
was
implemented
MATLAB
and
checked
by
generating
five
million
matrices
arbitrary
dimensions
up
30
random
entries
range
$\left[
-50,50\right]
$.
All
experiments
carried
out
have
revealed
method
works
correctly.
Article
Free
Access
Share
on
On
the
complexity
of
local
search
Authors:
C.
H.
Papadimitriou
Department
Computer
Science
and
Engineering,
University
California
at
San
Diego
DiegoView
Profile
,
A.
Schäffer
Science,
Rice
UniversityView
M.
Yannakakis
AT&T
Bell
Laboratories.
Laboratories.View
Authors
Info
&
Claims
STOC
'90:
Proceedings
twenty-second
annual
ACM
symposium
Theory
ComputingApril
1990Pages
438–445https://doi.org/10.1145/100216.100274Published:01
April
1990Publication
History
68citation1,337DownloadsMetricsTotal
Citations68Total
Downloads1,337Last
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Genetics,
Journal Year:
2019,
Volume and Issue:
212(1), P. 245 - 265
Published: March 4, 2019
Experiments
show
that
evolutionary
fitness
landscapes
can
have
a
rich
combinatorial
structure
due
to
epistasis.
For
some
landscapes,
this
produce
computational
constraint
prevents
evolution
from
finding
local
optima-thus
overturning
the
traditional
assumption
peaks
always
be
reached
quickly
if
no
other
forces
challenge
natural
selection.
Here,
I
introduce
distinction
between
easy
of
theory
where
found
in
moderate
number
steps,
and
hard
optima
requires
an
infeasible
amount
time.
Hard
examples
exist
even
among
with
reciprocal
sign
epistasis;
on
these
semismooth
strong
selection
weak
mutation
dynamics
cannot
find
unique
peak
polynomial
More
generally,
rugged
include
epistasis,
dynamics-even
ones
do
not
follow
adaptive
paths-can
optimum
quickly.
Moreover,
advantage
nearby
mutants
drop
off
exponentially
fast
but
has
power-law
long-term
experiments
associated
unbounded
growth
fitness.
Thus,
complexity
enables
open-ended
finite
landscapes.
Knowing
allows
us
use
tools
theoretical
computer
science
optimization
characterize
we
expect
see
nature.
present
candidates
for
at
scales
single
genes,
microbes,
complex
organisms
costly
learning
(Baldwin
effect)
or
maintained
cooperation
(Hankshaw
effect).
Just
how
ubiquitous
(and
corresponding
ultimate
evolution)
are
nature
becomes
open
empirical
question.
Mechanics of Advanced Materials and Structures,
Journal Year:
1994,
Volume and Issue:
1(1), P. 95 - 117
Published: Sept. 1, 1994
Abstract
This
paper
describes
the
use
of
a
genetic
algorithm
with
memory
for
design
minimum
thickness
composite
laminates
subject
to
strength,
buckling
and
ply
contiguity
constraints.
A
binary
tree
is
used
efficiently
store
retrieve
information
about
past
designs.
construct
set
linear
approximations
load
in
neighbourhood
each
member
population
The
are
then
seek
nearby
improved
designs
procedure
called
local
improvement.
demonstrates
that
this
substantially
reduces
number
analyses
required
search.
also
algorithms
helps
find
several
alternative
similar
performance,
thus
giving
designer
choice
alternatives.
INFORMS Journal on Computing,
Journal Year:
1994,
Volume and Issue:
6(2), P. 188 - 192
Published: May 1, 1994
We
consider
the
complexity
of
policy
improvement
algorithm
for
Markov
decision
processes.
show
that
four
variants
require
exponential
time
in
worst
case.
INFORMS
Journal
on
Computing,
ISSN
1091-9856,
was
published
as
ORSA
Computing
from
1989
to
1995
under
0899-1499.
