👂🎴 🕸️
<
div
>
The
term
heuristics
comes
from
the
Greek
word
heuriskein
''
which
means
to
find
or
to
discover
.
This
term
reflects
the
idea
of
finding
or
discovering
solutions
through
intuitive
or
trial
-
and
-
error
methods
. <
br
>
div
>
The
term
heuristics
comes
from
the
Greek
word
heuriskein
''
which
means
to
find
or
to
discover
.
This
term
reflects
the
idea
of
finding
or
discovering
solutions
through
intuitive
or
trial
-
and
-
error
methods
.
<
div
>
Mathematico
-
logical
heuristics
involve
using
structured
mathematical
or
logical
methods
to
solve
problems
.
They
include
techniques
like
calculus
for
optimizing
functions
''
linear
programming
for
maximizing
or
minimizing
linear
objectives
under
constraints
''
and
3
-
SAT
for
solving
complex
logical
puzzles
.
These
heuristics
apply
rigorous
mathematical
rules
and
logic
to
break
down
and
solve
problems
step
-
by
-
step
.
They
are
especially
useful
for
structured
problems
where
precise
''
logical
solutions
are
needed
''
like
in
operations
research
''
computer
science
''
and
engineering
.<
br
>
div
>
Nature
-
inspired
heuristics
are
problem
-
solving
methods
modeled
after
natural
processes
.
Like
how
birds
flock
or
bees
forage
''
these
algorithms
mimic
nature
to
tackle
complex
problems
.
They
use
strategies
like
evolution
''
ant
colony
behavior
''
or
bird
flocking
to
find
good
solutions
''
blending
randomness
with
specific
rules
from
nature
.
These
methods
are
useful
for
tough
problems
where
traditional
approaches
might
fail
''
creatively
applying
nature
'
s
wisdom
to
areas
like
computer
science
''
engineering
''
and
logistics
to
find
efficient
''
often
surprising
''
solutions
.
Human
heuristics
are
simple
''
intuitive
rules
we
use
to
make
quick
decisions
''
like
avoid
dark
alleys
at
night
.
They
are
based
on
our
experiences
and
common
sense
''
helping
us
navigate
everyday
choices
efficiently
without
much
thought
.
Useful
in
fast
-
paced
or
uncertain
situations
''
these
shortcuts
can
lead
to
good
enough
decisions
.
However
''
they
'
re
not
always
reliable
for
complex
''
critical
decisions
or
in
unfamiliar
contexts
''
as
they
can
oversimplify
situations
and
be
influenced
by
biases
''
potentially
leading
to
poor
choices
.
Parameters
&
Constraints
<
br
>
Middle
English
:
from
Old
French
constraindre
''
from
Latin
constringere
bind
tightly
together
’.
<
div
>
What
types
of
constraints
do
You
take
into
account
when
You
:
div
><
p
class
=
fragment
>
make
a
decision
D
p
><
p
class
=
fragment
>
when
You
solve
a
problem
P
 
p
><
p
class
=
fragment
>???
p
>
Constraint
programming
is
a
programming
paradigm
where
relationships
between
variables
are
expressed
as
constraints
.
The
objective
is
to
find
values
for
these
variables
that
satisfy
all
given
constraints
.
It
is
particularly
useful
for
solving
combinatorial
problems
''
such
as
scheduling
''
planning
''
and
resource
allocation
''
where
traditional
algorithms
might
be
inefficient
.
Instead
of
specifying
steps
to
achieve
a
solution
''
one
defines
the
desired
properties
of
a
solution
''
and
the
system
determines
a
valid
assignment
''
if
one
exists
.
Linear
Programming
(
LP
)
is
a
mathematical
method
used
to
find
the
best
outcome
in
a
model
whose
requirements
are
represented
by
linear
relationships
.
It
'
s
like
playing
a
game
where
you
need
to
achieve
the
highest
score
(
maximize
)
or
the
lowest
score
(
minimize
)
under
certain
rules
.
These
rules
are
your
constraints
''
like
how
much
money
you
can
spend
or
how
many
hours
you
have
.
The
score
you
'
re
trying
to
optimize
is
called
the
objective
function
''
and
it
'
s
also
a
linear
equation
.
LP
helps
you
figure
out
the
best
way
to
play
this
game
''
balancing
all
the
rules
''
to
achieve
your
goal
''
whether
it
'
s
making
the
most
profit
''
using
the
least
resources
''
or
something
similar
.
It
'
s
a
powerful
tool
for
decision
-
making
in
business
''
engineering
''
economics
''
and
more
.
<
p
class
=
fragment
>
Focus
:
CP
is
more
general
and
can
handle
a
wide
variety
of
constraints
''
not
just
linear
ones
.
It
can
deal
with
logical
conditions
''
like
either
-
or
situations
''
and
can
include
non
-
linear
relationships
.
p
><
p
class
=
fragment
>
Objective
:
CP
doesn
'
t
necessarily
have
an
objective
function
to
optimize
.
Instead
''
it
focuses
on
finding
solutions
that
satisfy
all
the
given
constraints
.
p
><
p
class
=
fragment
>
Method
:
It
uses
different
algorithms
than
LP
''
often
based
on
search
techniques
''
like
backtracking
or
heuristics
.
p
><
p
class
=
fragment
>
Constraints
:
Constraints
in
CP
can
be
diverse
-
linear
''
non
-
linear
''
logical
conditions
''
etc
.
For
example
''
a
constraint
could
be
that
a
certain
task
must
be
done
before
another
can
start
.
p
><
p
class
=
fragment
>
Solutions
:
Solutions
in
CP
are
often
discrete
(
like
whole
numbers
)
and
can
involve
deciding
between
different
options
or
scenarios
.
p
>
<
div
>
In
engineering
''
a
problem
P
is
defined
by
an
objective
function
that
needs
to
be
optimized
''
a
vector
of
parameters
that
can
be
adjusted
''
and
constraints
that
must
be
satisfied
.
div
><
div
><
br
>
div
><
div
>
The
solution
S
is
the
optimal
set
of
parameter
values
that
achieve
the
desired
optimization
while
staying
within
the
bounds
of
the
constraints
.
div
>
What
problem
is
worth
time
of
Your
life
?<
br
>
A
problem
P
can
be
defined
as
a
situation
or
scenario
where
a
particular
system
''
device
''
or
process
is
not
performing
as
desired
''
leading
to
suboptimal
or
undesired
outcomes
.
In
a
design
&
engineering
context
''
P
is
typically
framed
in
terms
of
:<
p
class
=
fragment
>
Vector
of
Parameters
:
This
is
a
set
of
variables
or
conditions
that
characterize
the
system
or
scenario
.
They
can
be
input
conditions
''
system
states
''
or
any
other
relevant
metrics
that
describe
the
system
p
><
p
class
=
fragment
>
Objective
Function
:
This
is
a
mathematical
function
that
quantifies
how
far
the
current
system
'
s
performance
is
from
the
desired
performance
.
The
goal
is
usually
to
minimize
or
maximize
this
function
p
><
p
class
=
fragment
>
Constraints
:
These
are
the
bounds
or
limitations
within
which
the
system
operates
.
In
engineering
problems
''
constraints
can
arise
from
physical
limitations
''
safety
requirements
''
budgetary
restrictions
''
etc
.
The
solution
must
satisfy
these
constraints
.
p
>
 
<
br
>
In
multi
-
objective
optimization
''
optimize
for
two
or
more
objectives
in
the
same
time
.
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