Benchmarks for model-based optimization.
Project description
Evobench
Evobench is a collection of benchmark problems dedicated for model-based large scale optimization.
Overview
This package contains following problems.
Discrete
- trap
- step trap
- bimodal
- step bimodal
- HIFF
- Ising Spin Glass
Continuous
- trap
- step trap
- multimodal
- step multimodal
- sawtooth
Compound
You can create your own benchmark made of other benchmarks.
Getting started
pip install evobench
import evobench
trap = evobench.discrete.Trap(blocks=[4, 4, 4])
population = trap.initialize_population(population_size=1e3)
fitness = trap.evaluate_population(population)
You can also evaluate single solution.
fitness = trap.evaluate_solution(population.solutions[0])
Every time you're evaluating solutions we increment ffe counter.
Solution is not evaluated again, if it didn't change.
You can access it through benchmark
instance.
print(trap.ffe)
Ising Spin Glass
To instantiate ISG you need to pass specific problem configuration.
from evobench.discrete import IsingSpinGlass
isg = IsingSpinGlass('IsingSpinGlass_pm_16_0')
You can find 5,000 instances at evobench\discrete\isg\data
folder. Instances vary in length and complexity.
Compound Benchmark
Creating your own compound benchmarks is really easy.
You just need to define your sub-benchmarks and pass them as a list. All other fuctions work just the same as with the normal Benchmark
.
from evobench import CompoundBenchmark
from evobench import continuous, discrete
benchmark = CompoundBenchmark(
benchmarks=[
discrete.Trap(blocks=[5, 2, 4]),
continuous.Trap(blocks=[3, 6, 4])
],
use_shuffle=True,
multiprocessing=True,
verbose=1
)
population = benchmark.initialize_population(population_size=1000)
benchmark.evaluate_population(population)
How to implement your own function
Fully separable
You need to inherit Separable
class from evobench.separable
.
Then just implement def evaluate_block(self, block: np.ndarray) -> int
method. Best follow evobench.discrete.trap
implementation.
Other
Inherit Benchmark
class from evobench.benchmark
. Then implement def _evaluate_solution(self, solution: Solution) -> float
method.
Linkage quality
Linkage quality metrics are located at evobench.linkage.metrics
.
Available metrics:
- fill quality
Coming soon
We'll be adding more problems in the near future. If you're looking for any particular problem, please mail us or open an issue. We're working on linkage quality metrics. Once they're published, we'll be incorporating them to this package.
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