Evaluate solutions¶
The lifecycle of metaheuristics often requires to evaluate list of solutions on every iteration. In evolutionary algorithms, for example, this list of solutions is known as population.
In order to evaluate a population, NSGA-II (and in general, any generational algorithms in jMetalPy) uses an evaluator object.
Sequential¶
The default evaluator runs in a sequential fashion (i.e., one solution at a time):
from jmetal.util.evaluator import SequentialEvaluator
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
...
population_evaluator = SequentialEvaluator(),
)
Parallel and distributed¶
Solutions can also be evaluated in parallel, using threads or processes:
from jmetal.util.evaluator import MapEvaluator
from jmetal.util.evaluator import MultiprocessEvaluator
jMetalPy includes an evaluator based on Apache Spark, so the solutions can be evaluated in a variety of parallel systems (multicores, clusters):
from jmetal.util.evaluator import SparkEvaluator
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
...
population_evaluator = SparkEvaluator(processes=8),
)
Or by means of Dask:
from jmetal.util.evaluator import DaskEvaluator
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
...
population_evaluator = DaskEvaluator(),
)
Warning
SparkEvaluator and DaskEvaluator requires pySpark and Dask, respectively.
API¶
-
class
jmetal.util.evaluator.MapEvaluator(processes: int = None)[source]¶ Bases:
jmetal.util.evaluator.Evaluator
-
class
jmetal.util.evaluator.MultiprocessEvaluator(processes: int = None)[source]¶ Bases:
jmetal.util.evaluator.Evaluator