Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

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Introduction

Gradient-Free-Optimizers provides a collection of easy to use optimization techniques, whose objective function only requires an arbitrary score that gets maximized. This makes gradient-free methods capable of solving various optimization problems, including:

Optimizing arbitrary mathematical functions.
Fitting multiple gauss-distributions to data.
Hyperparameter-optimization of machine-learning methods.

Gradient-Free-Optimizers is the optimization backend of Hyperactive (in v3.0.0 and higher) but it can also be used by itself as a leaner and simpler optimization toolkit.

Optimization algorithms • Installation • Examples • API reference • Roadmap

Main features

Easy to use:
Simple API-design

You can optimize anything that can be defined in a python function. For example a simple parabola function:
```
def objective_function(para):
    score = para["x1"] * para["x1"]
    return -score
```
Define where to search via numpy ranges:
```
search_space = {
    "x": np.arange(0, 5, 0.1),
}
```
That`s all the information the algorithm needs to search for the maximum in the objective function:
```
from gradient_free_optimizers import RandomSearchOptimizer

opt = RandomSearchOptimizer(search_space)
opt.search(objective_function, n_iter=100000)
```
Receive prepared information about ongoing and finished optimization runs

During the optimization you will receive ongoing information in a progress bar:
- current best score
- the position in the search space of the current best score
- the iteration when the current best score was found
- other information about the progress native to tqdm
High performance:

Modern optimization techniques

Gradient-Free-Optimizers provides not just meta-heuristic optimization methods but also sequential model based optimizers like bayesian optimization, which delivers good results for expensive objetive functions like deep-learning models.

Lightweight backend

Even for the very simple parabola function the optimization time is about 60% of the entire iteration time when optimizing with random search. This shows, that (despite all its features) Gradient-Free-Optimizers has an efficient optimization backend without any unnecessary slowdown.
Save time with memory dictionary

Per default Gradient-Free-Optimizers will look for the current position in a memory dictionary before evaluating the objective function.
- If the position is not in the dictionary the objective function will be evaluated and the position and score is saved in the dictionary.
- If a position is already saved in the dictionary Gradient-Free-Optimizers will just extract the score from it instead of evaluating the objective function. This avoids reevaluating computationally expensive objective functions (machine- or deep-learning) and therefore saves time.
High reliability:
Extensive testing

Gradient-Free-Optimizers is extensivly tested with more than 400 tests in 2500 lines of test code. This includes the testing of:
- Each optimization algorithm
- Each optimization parameter
- All attributes that are part of the public api
Performance test for each optimizer

Each optimization algorithm must perform above a certain threshold to be included. Poorly performing algorithms are reworked or scraped.

Optimization algorithms:

Gradient-Free-Optimizers supports a variety of optimization algorithms, which can make choosing the right algorithm a tedious endeavor. The gifs in this section give a visual representation how the different optimization algorithms explore the search space and exploit the collected information about the search space for a convex and non-convex objective function. More detailed explanations of all optimization algorithms can be found in the official documentation.

Local Optimization

Hill Climbing

Evaluates the score of n neighbours in an epsilon environment and moves to the best one.

Convex Function	Non-convex Function

Stochastic Hill Climbing

Adds a probability to the hill climbing to move to a worse position in the search-space to escape local optima.

Convex Function	Non-convex Function

Repulsing Hill Climbing

Hill climbing algorithm with the addition of increasing epsilon by a factor if no better neighbour was found.

Convex Function	Non-convex Function

Simulated Annealing

Adds a probability to the hill climbing to move to a worse position in the search-space to escape local optima with decreasing probability over time.

Convex Function	Non-convex Function

Downhill Simplex Optimization

Constructs a simplex from multiple positions that moves through the search-space by reflecting, expanding, contracting or shrinking.

Convex Function	Non-convex Function

Global Optimization

Random Search

Moves to random positions in each iteration.

Convex Function	Non-convex Function

Grid Search

Grid-search that moves through search-space diagonal (with step-size=1) starting from a corner.

Convex Function	Non-convex Function

Random Restart Hill Climbing

Hill climbingm, that moves to a random position after n iterations.

Convex Function	Non-convex Function

Random Annealing

Hill Climbing, that has large epsilon at the start of the search decreasing over time.

Convex Function	Non-convex Function

Pattern Search

Creates cross-shaped collection of positions that move through search-space by moving as a whole towards optima or shrinking the cross.

Convex Function	Non-convex Function

Powell's Method

Optimizes each search-space dimension at a time with a hill-climbing algorithm.

Convex Function	Non-convex Function

Population-Based Optimization

Parallel Tempering

Population of n simulated annealers, which occasionally swap transition probabilities.

Convex Function	Non-convex Function

Particle Swarm Optimization

Population of n particles attracting each other and moving towards the best particle.

Convex Function	Non-convex Function

Spiral Optimization

Population of n particles moving in a spiral pattern around the best position.

Convex Function	Non-convex Function

Genetic Algorithm

Evolutionary algorithm selecting the best individuals in the population, mixing their parameters to get new solutions.

Convex Function	Non-convex Function

Evolution Strategy

Population of n hill climbers occasionally mixing positional information and removing worst positions from population.

Convex Function	Non-convex Function

Differential Evolution

Improves a population of candidate solutions by creating trial vectors through the differential mutation of three randomly selected individuals.

Convex Function	Non-convex Function

Sequential Model-Based Optimization

Bayesian Optimization

Gaussian process fitting to explored positions and predicting promising new positions.

Convex Function	Non-convex Function

Lipschitz Optimization

Calculates an upper bound from the distances of the previously explored positions to find new promising positions.

Convex Function	Non-convex Function

DIRECT algorithm

Separates search space into subspaces. It evaluates the center position of each subspace to decide which subspace to sepate further.

Convex Function	Non-convex Function

Tree of Parzen Estimators

Kernel density estimators fitting to good and bad explored positions and predicting promising new positions.

Convex Function	Non-convex Function

Forest Optimizer

Ensemble of decision trees fitting to explored positions and predicting promising new positions.

Convex Function	Non-convex Function

Sideprojects and Tools

The following packages are designed to support Gradient-Free-Optimizers and expand its use cases.

Package	Description
Search-Data-Collector	Simple tool to save search-data during or after the optimization run into csv-files.
Search-Data-Explorer	Visualize search-data with plotly inside a streamlit dashboard.

If you want news about Gradient-Free-Optimizers and related projects you can follow me on twitter.

Installation

The most recent version of Gradient-Free-Optimizers is available on PyPi:

pip install gradient-free-optimizers

Examples

Convex function

import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer


def parabola_function(para):
    loss = para["x"] * para["x"]
    return -loss


search_space = {"x": np.arange(-10, 10, 0.1)}

opt = RandomSearchOptimizer(search_space)
opt.search(parabola_function, n_iter=100000)

Non-convex function

import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer


def ackley_function(pos_new):
    x = pos_new["x1"]
    y = pos_new["x2"]

    a1 = -20 * np.exp(-0.2 * np.sqrt(0.5 * (x * x + y * y)))
    a2 = -np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
    score = a1 + a2 + 20
    return -score


search_space = {
    "x1": np.arange(-100, 101, 0.1),
    "x2": np.arange(-100, 101, 0.1),
}

opt = RandomSearchOptimizer(search_space)
opt.search(ackley_function, n_iter=30000)

Machine learning example

import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_wine

from gradient_free_optimizers import HillClimbingOptimizer


data = load_wine()
X, y = data.data, data.target


def model(para):
    gbc = GradientBoostingClassifier(
        n_estimators=para["n_estimators"],
        max_depth=para["max_depth"],
        min_samples_split=para["min_samples_split"],
        min_samples_leaf=para["min_samples_leaf"],
    )
    scores = cross_val_score(gbc, X, y, cv=3)

    return scores.mean()


search_space = {
    "n_estimators": np.arange(20, 120, 1),
    "max_depth": np.arange(2, 12, 1),
    "min_samples_split": np.arange(2, 12, 1),
    "min_samples_leaf": np.arange(1, 12, 1),
}

opt = HillClimbingOptimizer(search_space)
opt.search(model, n_iter=50)

Constrained Optimization example

import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer


def convex_function(pos_new):
    score = -(pos_new["x1"] * pos_new["x1"] + pos_new["x2"] * pos_new["x2"])
    return score


search_space = {
    "x1": np.arange(-100, 101, 0.1),
    "x2": np.arange(-100, 101, 0.1),
}


def constraint_1(para):
    # only values in 'x1' higher than -5 are valid
    return para["x1"] > -5


# put one or more constraints inside a list
constraints_list = [constraint_1]


# pass list of constraints to the optimizer
opt = RandomSearchOptimizer(search_space, constraints=constraints_list)
opt.search(convex_function, n_iter=50)

search_data = opt.search_data

# the search-data does not contain any samples where x1 is equal or below -5
print("\n search_data \n", search_data, "\n")

Roadmap

v0.3.0 ✔️

add sampling parameter to Bayesian optimizer
add warnings parameter to Bayesian optimizer
improve access to parameters of optimizers within population-based-optimizers (e.g. annealing rate of simulated annealing population in parallel tempering)

v0.4.0 ✔️

add early stopping parameter

v0.5.0 ✔️

add grid-search to optimizers
impoved performance testing for optimizers

v1.0.0 ✔️

Finalize API (1.0.0)
add Downhill-simplex algorithm to optimizers
add Pattern search to optimizers
add Powell's method to optimizers
add parallel random annealing to optimizers
add ensemble-optimizer to optimizers

v1.1.0 ✔️

add Spiral Optimization
add Lipschitz Optimizer
print the random seed for reproducibility

v1.2.0 ✔️

add DIRECT algorithm
automatically add random initial positions if necessary (often requested)

v1.3.0 ✔️

add support for constrained optimization

v1.4.0 ✔️

add Grid search parameter that changes direction of search
add SMBO parameter that enables to avoid replacement of the sampling

v1.5.0 ✔️

add Genetic Algorithm
add Differential evolution

v1.6.0 ✔️

add support for numpy v2
add support for pandas v2
add support for python 3.12
transfer setup.py to pyproject.toml
change project structure to src-layout

Future releases

add Ant-colony optimization
add Harmonic-serch
add API, testing and doc to (better) use GFO as backend-optimization package
add Random search parameter that enables to avoid replacement of the sampling
add other acquisition functions to smbo (Probability of improvement, Entropy search, ...)

Gradient Free Optimizers <=> Hyperactive

Gradient-Free-Optimizers was created as the optimization backend of the Hyperactive package. Therefore the algorithms are exactly the same in both packages and deliver the same results. However you can still use Gradient-Free-Optimizers as a standalone package. The separation of Gradient-Free-Optimizers from Hyperactive enables multiple advantages:

Even easier to use than Hyperactive
Separate and more thorough testing
Other developers can easily use GFOs as an optimizaton backend if desired
Better isolation from the complex information flow in Hyperactive. GFOs only uses positions and scores in a N-dimensional search-space. It returns only the new position after each iteration.
a smaller and cleaner code base, if you want to explore my implementation of these optimization techniques.

While Gradient-Free-Optimizers is relatively simple, Hyperactive is a more complex project with additional features to make optimization of computationally expensive models (like engineering simulation or machine-/deep-learning models) more convenient.

Citation

@Misc{gfo2020,
  author =   {{Simon Blanke}},
  title =    {{Gradient-Free-Optimizers}: Simple and reliable optimization with local, global, population-based and sequential techniques in numerical search spaces.},
  howpublished = {\url{https://github.com/SimonBlanke}},
  year = {since 2020}
}

License

Gradient-Free-Optimizers is licensed under the following License:

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README.md

README.md

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Introduction

Optimization algorithms • Installation • Examples • API reference • Roadmap

Main features

Optimization algorithms:

Local Optimization

Global Optimization

Population-Based Optimization

Sequential Model-Based Optimization

Sideprojects and Tools

Installation

Examples

Roadmap

Gradient Free Optimizers <=> Hyperactive

Citation

License

Files

README.md

Latest commit

History

README.md

File metadata and controls

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Introduction

Optimization algorithms • Installation • Examples • API reference • Roadmap

Main features

Optimization algorithms:

Local Optimization

Global Optimization

Population-Based Optimization

Sequential Model-Based Optimization

Sideprojects and Tools

Installation

Examples

Roadmap

Gradient Free Optimizers <=> Hyperactive

Citation

License