👂 🎴 🕸️
A
Genetic
Algorithm
is
a
method
in
evolutionary
optimization
that
solves
problems
by
mimicking
natural
evolution
.
Imagine
a
survival
contest
where
each
participant
(
solution
)
has
traits
(
parameters
).
These
solutions
breed
and
mutate
''
creating
new
generations
.
The
fittest
solutions
''
judged
by
a
fitness
function
''
survive
to
breed
again
.
Over
time
''
this
process
'
evolves
'
increasingly
effective
solutions
.
It
'
s
like
nature
'
s
trial
-
and
-
error
but
used
for
complex
problems
like
route
planning
''
where
finding
the
best
or
a
good
-
enough
solution
is
essential
.
In
evolutionary
optimization
''
replication
is
like
making
copies
of
the
best
solutions
.
Imagine
a
survival
contest
where
top
performers
are
cloned
.
These
copies
then
undergo
changes
(
mutations
)
or
combine
features
(
crossover
)
to
create
new
solutions
.
Replication
ensures
good
traits
are
passed
on
''
increasing
chances
that
future
generations
will
perform
even
better
.
It
'
s
used
in
algorithms
to
solve
complex
problems
''
where
keeping
and
tweaking
successful
solutions
gradually
leads
to
finding
the
best
or
a
very
good
answer
.
In
evolutionary
optimization
''
variation
is
the
process
of
introducing
diversity
into
the
population
of
solutions
.
Like
genetic
mutations
and
breeding
in
nature
''
it
involves
altering
the
'
genes
'
(
parameters
)
of
candidate
solutions
to
create
new
''
different
ones
.
This
can
be
done
through
mutation
(
changing
some
parameters
)
or
crossover
(
mixing
parameters
from
two
solutions
).
Variation
is
crucial
for
exploring
new
solutions
and
avoiding
getting
stuck
with
suboptimal
ones
''
much
like
how
biological
diversity
is
key
to
the
survival
and
evolution
of
species
.
In
evolutionary
optimization
''
selection
is
like
a
survival
test
for
candidate
solutions
''
deciding
which
ones
get
to
'
reproduce
.'
Selection
operators
are
the
rules
determining
who
passes
this
test
.
They
might
choose
the
fittest
solutions
(
those
solving
the
problem
best
)
or
sometimes
include
random
or
less
fit
ones
for
diversity
. 
Genetic
Programming
(
GP
)
is
a
type
of
evolutionary
optimization
where
programs
themselves
evolve
to
solve
problems
.
Imagine
a
computer
automatically
writing
and
modifying
its
own
code
to
get
better
at
a
task
.
In
GP
''
each
'
individual
'
is
a
computer
program
.
These
programs
are
tested
for
their
ability
to
solve
a
problem
''
and
the
best
ones
are
modified
(
mutated
)
or
combined
(
crossed
over
)
to
create
new
programs
.
Over
generations
''
this
process
evolves
programs
that
become
increasingly
effective
at
the
task
.
Grammar
Evolution
is
a
type
of
evolutionary
optimization
where
solutions
are
generated
using
a
predefined
set
of
rules
''
like
a
grammar
in
language
.
Imagine
creating
sentences
using
grammar
rules
''
but
here
''
the
'
sentences
'
are
computer
programs
or
formulas
.
Each
solution
must
follow
these
rules
''
ensuring
they
make
sense
.
Like
in
genetic
programming
''
these
solutions
evolve
over
generations
''
becoming
better
at
solving
a
problem
.
It
'
s
particularly
useful
when
solutions
need
a
specific
structure
or
format
''
allowing
for
complex
''
yet
orderly
''
evolution
.
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