<s>
In	O
evolutionary	O
computation	O
,	O
Minimum	B-Algorithm
Population	I-Algorithm
Search	I-Algorithm
(	O
MPS	O
)	O
is	O
a	O
computational	O
method	O
that	O
optimizes	O
a	O
problem	O
by	O
iteratively	O
trying	O
to	O
improve	O
a	O
set	O
of	O
candidate	O
solutions	O
with	O
regard	O
to	O
a	O
given	O
measure	O
of	O
quality	O
.	O
</s>
<s>
MPS	O
is	O
a	O
metaheuristic	B-Algorithm
as	O
it	O
makes	O
few	O
or	O
no	O
assumptions	O
about	O
the	O
problem	O
being	O
optimized	O
and	O
can	O
search	O
very	O
large	O
spaces	O
of	O
candidate	O
solutions	O
.	O
</s>
<s>
For	O
problems	O
where	O
finding	O
the	O
precise	O
global	O
optimum	O
is	O
less	O
important	O
than	O
finding	O
an	O
acceptable	O
local	O
optimum	O
in	O
a	O
fixed	O
amount	O
of	O
time	O
,	O
using	O
a	O
metaheuristic	B-Algorithm
such	O
as	O
MPS	O
may	O
be	O
preferable	O
to	O
alternatives	O
such	O
as	O
brute-force	B-Algorithm
search	I-Algorithm
or	O
gradient	B-Algorithm
descent	I-Algorithm
.	O
</s>
<s>
MPS	O
is	O
used	O
for	O
multidimensional	O
real-valued	O
functions	O
but	O
does	O
not	O
use	O
the	O
gradient	O
of	O
the	O
problem	O
being	O
optimized	O
,	O
which	O
means	O
MPS	O
does	O
not	O
require	O
for	O
the	O
optimization	O
problem	O
to	O
be	O
differentiable	O
as	O
is	O
required	O
by	O
classic	O
optimization	O
methods	O
such	O
as	O
gradient	B-Algorithm
descent	I-Algorithm
and	O
quasi-newton	B-Algorithm
methods	I-Algorithm
.	O
</s>
<s>
In	O
a	O
similar	O
way	O
to	O
Differential	B-Algorithm
evolution	I-Algorithm
,	O
MPS	O
uses	O
difference	O
vectors	O
between	O
the	O
members	O
of	O
the	O
population	O
in	O
order	O
to	O
generate	O
new	O
solutions	O
.	O
</s>
<s>
Thresheld	O
Convergence	O
has	O
been	O
successfully	O
applied	O
to	O
several	O
population-based	O
metaheuristics	B-Algorithm
such	O
as	O
Particle	B-Algorithm
Swarm	I-Algorithm
Optimization	I-Algorithm
,	O
Differential	B-Algorithm
evolution	I-Algorithm
,	O
Evolution	B-Algorithm
strategies	I-Algorithm
,	O
Simulated	B-Algorithm
annealing	I-Algorithm
and	O
Estimation	O
of	O
Distribution	O
Algorithms	O
.	O
</s>
