evolution-opt 0.0.4

Evolution inspired optimisation algorithms

Description

This package is a Python aims at providing a range of nature-inspired optimisation algorithms. This first version includes an implementation of genetic algorithm with "regularized evolution".

Installation

This package can be installed with "pip" or by cloning this repository

$ pip install evolution_opt

Example Usage

1) Define a function to be optimised

This function has to take a dictionary of parameter as argument:

def difficult_problem(param_dict):
    result = param_dict['x']**2 + (param_dict['y']+1)**2
    if param_dict['luck'] == 'lucky':
        pass
    else:
        result += 10
    return result

This function could be any process that takes parameters as input and outputs a scalar value.

It could evaluate a model's cross-validation score based on given hyperparameter values, a profit/cost function, the efficiency of a resourcing plan... The possibilities are limitless.

2) Define a search space

search_space = [
    Integer(-100,100, 'x'),
    Real(-100,100, 'y'),
    Categorical(['lucky', 'unlucky'], 'luck')
]

The search space can be composed of Integer, Real and Categorical variables. Numeric parameters are initialised with a lower bound, upper bound and a parameter name. Categorical parameters require a list of possible values and a parameter name.

3) Run the evolutionary algorithm

best_params = optimise(difficult_problem,search_space,minimize=True, 
                           population_size=20,n_rounds=500)   

# Prints:
# Number of Iterations: 500
# Best score: 0.00410559779230605
# Best parameters: {'x': -0.0, 'y': -1.0640749388786759, 'luck': 'lucky'}