Approximating the hypervolume#
Determining the hypervolume indicator is a computationally expensive task. Even in case of a reasonably small dimension and low number of points (e.g. 100 points in 10 dimensions), there are currently no known algorithms that can yield the results fast enough for use in most multiple-objective optimizers.
In this tutorial we will show a way to compute the hypervolume indicator faster, but at the cost of accuracy. Two algorithms are included in pygmo and are capable of computing the hypervolume approximately:
bf_fpras- capable of approximating the hypervolume indicator
bf_approx- capable of approximating the least and the greatest contributor
population object will never delegate the computation to any of the approximated algorithms.
The only way to use the approximated algorithms is through the explicit request
(see the beginning of the tutorial Advanced hypervolume computation and analysis for
more information on how to do that).
bf_fpras found in PyGMO is a Fully Polynomial-Time Randomized Approximation Scheme accustomed
for the computation of the hypervolume indicator. You can invoke the FPRAS as follows:
>>> import pygmo as pg >>> prob = pg.problem(pg.dtlz(prob_id = 3, fdim=10, dim=11)) >>> pop = pg.population(prob, 100) >>> fpras = pg.bf_fpras(eps=0.1, delta=0.1) >>> hv = pg.hypervolume(pop) >>> offset = 5 >>> ref_point = hv.refpoint(offset = 0.1) >>> hv.compute(ref, hv_algo=fpras)
To influence the accuracy of the FPRAS, it is possible to provide the following keyword arguments to its constructor:
eps - relative accuracy of the approximation
delta - probability of error
For given parameters eps=eps0 and delta=delta0, the obtained solution is (with probability 1 - delta0) within a factor of 1 +/- eps0 from the exact hypervolume.
The smaller the eps and delta, the longer it will take for the algorithm to evaluate.
By the relative error, we mean the scenario in which the approximation is accurate within given order of magnitude, e.g. 312.32 and 313.41, are accurate within eps = 0.1, because they are accurate within two orders of magnitude. At the same time, these are NOT accurate within eps = 0.01.
The plots presents the measured running time (average and MAX out of 10) of FPRAS for varying
Front size and
The algorithm is instantiated with eps=0.1 and delta=0.1.
Notice the lack of any exponential increase in time as the dimension increases.
Since FPRAS scales so well with the dimension size, we can also present data for larger front sizes and number of objectives.
Now, that is quite a feat! A front of 1000 points in 100 dimensions would be beyond the reach of any of the algorithms that rely on the exact computation.
Approximation of the least contributor#
Additionally to FPRAS, pygmo provides an approximated algorithm dedicated for the computation of the least/greatest contributor. This is useful when we want to utilize evolutionary algorithms which rely on that feature, especially when the problems has many objectives.
>>> # Problem with 30 objectives and 300 individuals >>> prob = pg.problem(pg.dtlz(prob_id = 3, fdim=30, dim=35)) >>> pop = pg.population(prob, size = 300) >>> hv_algo = pg.bf_approx(eps=0.1, delta=0.1) >>> hv = pg.hypervolume(pop) >>> offset = 5 >>> ref_point = hv.refpoint(offset = 0.1) >>> hv.least_contributor(ref_point, hv_algo=hv_algo)
bf_approx provides only two features - computation of the least and the greatest contributor.
Request for the computation of any other measure will raise and exception.