Closed michelesaputo closed 1 year ago
Hi Michele, You need to define the constraints in your problem. Follow this example for instance and notice the return statement containing two constraints:
from platypus import NSGAII, Problem, Real
def belegundu(vars):
x = vars[0]
y = vars[1]
return [-2*x + y, 2*x + y], [-x + y - 1, x + y - 7]
problem = Problem(2, 2, 2)
problem.types[:] = [Real(0, 5), Real(0, 3)]
problem.constraints[:] = "<=0"
problem.function = belegundu
algorithm = NSGAII(problem)
algorithm.run(10000)
And to get the most optimal point you can query your result for smallest or largest objective score. Kinda of like this:
objectives = [s.objectives[:] for s in result]
For algorithms you can compare using the experimenter
to see what kind of results you get.
Hope that helps.
Hi, that's a good hint, thank you. as I wrote some other code i noticed that if i write the code like this
from platypus import NSGAII, Problem, Real
def belegundu(vars): x = vars[0] y = vars[1] return [-2x + y, 2x + y], [-x + y - 1, x + y - 7]
problem = Problem(2, 2, 2) problem.types[:] = [Real(0, 5), Real(0, 3)] problem.constraints[:] = "<=0" problem.function = belegundu
algorithm = NSGAII(problem) algorithm.run(10000) objectives1 = [solution.objectives[0] for solution in algorithm.result] objectives2 = [solution.objectives[1] for solution in algorithm.result] print(len(objectives1)) print(objectives1) print(len(objectives2)) print(objectives2)
arrays objectives1 and objectives2 consist of 100 elements regardless of the number of runs. how can i increase the number of runs, if there's a way to do so?
Thank you, Michele
Il giorno gio 6 feb 2020 alle ore 22:30 mronda notifications@github.com ha scritto:
Hi Michele, You need to define the constraints in your problem. Follow this example for instance and notice the return statement containing two constraints:
from platypus import NSGAII, Problem, Real
def belegundu(vars): x = vars[0] y = vars[1] return [-2x + y, 2x + y], [-x + y - 1, x + y - 7]
problem = Problem(2, 2, 2) problem.types[:] = [Real(0, 5), Real(0, 3)] problem.constraints[:] = "<=0" problem.function = belegundu
algorithm = NSGAII(problem) algorithm.run(10000)
And to get the most optimal point you can query your result for smallest or largest objective score. Kinda of like this: objectives = [s.objectives[:] for s in result]
For algorithms you can compare using the experimenter to see what kind of results you get. Hope that helps.
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You are seeing 100 results since the population_size argument is 100 by default. You can increase this with NSGAII(problem, population_size=250)
for example.
Thank you for the response, i don't understand exactly whic are the differences in
algorithm = NSGAII(problem,population_size=250)
algorithm.run(1000)
is that 250 a number of evaluation randomly picked after the algorithm completion or the meaning is different?
Thank you for you patience, Michele
Il giorno gio 13 feb 2020 alle ore 17:25 David Hadka < notifications@github.com> ha scritto:
You are seeing 100 results since the population_size argument is 100 by default. You can increase this with NSGAII(problem, population_size=250) for example.
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Hi Michele,
The algo.run(n)
is the number of function evaluations. So if your population is 250 and you run algo.run(condition=250) it will just evaluate those individuals without mutation/crossover. You can check the TerminationConditions to see how that works. Hope that helps.
This issue is stale and will be closed soon. If you feel this issue is still relevant, please comment to keep it active. Please also consider working on a fix and submitting a PR.
Hi, as I'm trying to get more and more into coding i was interested into use platypuc for multiple response optimization in order to find a global optimum given a set of n>2 equations and constraint.
I tried to do that with simple equation but i didn't have any luck. Any suggestion about the algorithm to be used or the code line to be used?
here's the piece of code without any tweaks
MRO_rev1 - Copia.txt
Thank you, Michele