Benchmarks for large scale, model-based optimization.
Evobench is a collection of benchmark problems dedicated for model-based large scale optimization.
This package contains following problems.
- step trap
- step bimodal
- Ising Spin Glass
- step multimodal
pip install evobench
import evobench trap = evobench.discrete.Trap(block_size=5, repetitions=3) initialization = evobench.discrete.initialization.Uniform(population_size=4e3) population = initialization.initialize_population(trap.genome_size) fitness = trap.evaluate_population(population)
You can also evaluate single solution.
fitness = trap.evaluate_solution(population.solutions)
Every time you're evaluating solutions we increment ffe counter.
You can access it through
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.
How to implement your own function
You need to inherit
Separable class from
Then just implement
def evaluate_block(self, block: np.ndarray) -> int method. Best follow
Benchmark class from
evobench.benchmark. Then implement
def _evaluate_solution(self, solution: Solution) -> float method.
We'll be adding more problems in near future. If you're looking for any particular problem, please mail us or open an issue.
We're thinking about interactive visualizations, so you can sample the space and check how it looks. It's easier than digging through definitions.
We're working on linkage quality metrics. Once they're published, we'll incorporate them to this package.
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