Benchmarks for large scale, 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
Continous
- trap
- multimodal
- step multimodal
- sawtooth
Getting started
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[0])
Everytime you're evaluating solutions we increment ffe counter.
You can access it through benchmark
instance.
print(trap.ffe)
Coming soon
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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