👂 🎴 🕸️
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
. 
<
p
class
=
„
fragment
“
>
elitism
:
select
N
most
fit
individuals
and
copy
them
to
next
generation
p
><
p
class
=
„
fragment
“
>
roulette
-
wheel
:
probability
of
survival
into
next
generation
is
proportional
to
indvididual
'
s
fitness
p
><
p
class
=
„
fragment
“
>
tournament
...
p
>
In
evolutionary
optimization
''
a
„
fitness
function
“
is
like
a
scoring
system
that
rates
how
good
each
candidate
solution
(
or
'
individual
')
is
at
solving
the
problem
.
Think
of
it
as
a
judge
in
a
competition
''
evaluating
participants
based
on
how
well
they
meet
certain
criteria
.
The
higher
the
score
''
the
better
the
solution
.
This
function
is
crucial
because
it
guides
the
selection
process
''
helping
to
decide
which
solutions
should
be
kept
''
discarded
''
or
combined
to
create
the
next
generation
of
solutions
.
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
.
Crossover
<
p
class
=
„
fragment
“
>
for
numeric
genes
:
additive
mutation
''
multiplicative
mutation
''
complex
(
imaginary
)
mutation
p
><
p
class
=
„
fragment
“
>
for
symbolic
ones
:
removal
''
addition
or
replacement
of
a
symbol
;
metathesis
(
e
.
g
.
MORF
->
FORM
)
p
><
p
class
=
„
fragment
“
>
NOTE
:
Mutation
rates
often
fall
in
the
range
of
0
.
5
%
to
1
%
per
gene
.
This
rate
is
low
enough
to
prevent
excessive
random
search
(
which
can
disrupt
good
solutions
)
and
high
enough
to
introduce
diversity
and
enable
the
algorithm
to
explore
new
areas
of
the
solution
space
.
p
>
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
.
Population
is
a
set
of
individuals
.
<
div
>
In
evolutionary
optimization
''
an
„
individual
''
“
also
termed
a
„
genotype
“
or
„
chromosome
''
“
is
a
candidate
solution
to
a
problem
.
Think
of
it
like
a
recipe
where
each
ingredient
is
a
parameter
of
the
solution
.
These
parameters
are
the
„
genes
“
of
the
individual
.
Together
''
they
define
how
the
solution
behaves
or
performs
.
Like
in
natural
evolution
''
these
individuals
can
be
altered
(
mutated
)
or
combined
(
through
crossover
)
to
create
new
solutions
''
in
the
quest
to
find
the
best
or
most
effective
answer
.<
br
>
div
>
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