pyparadiseo 0.3

Python version of the ParadisEO framework for heuristic optimization

Table of Contents

• Installation
• Usage
• Components

Installation

For now, PyParadiseo is only available for Python 3.6. You can install PyParadiseo with pip

pip install pyparadiseo

Getting Started

The documentation of PyParadiseo is available here

Example of running EO's simple GA (SGA) for the One-Max test problem

from pyparadiseo import Pop
from pyparadiseo.evaluator import FitnessEval,PopLoopEval

from pyparadiseo import evaluator
from pyparadiseo import operator
from pyparadiseo import population
from pyparadiseo import initializer

from pyparadiseo.eo import algo,select_one,continuator

import numpy as np

if __name__ == "__main__":
    #set solution type globally
    config.set_solution_type('bin')

    #make pyparadiseo evaluator from python function
    eval = evaluator.fitness(lambda sol: np.count_nonzero(sol))

    #generate and evaluate population
    init = initializer.random(size=20)
    pop = population.from_init(25, init)
    pop_eval=evaluator.pop_eval_from_fitness(eval)
    pop_eval(pop,pop)

    #assemble simple GA
    sga = algo.simpleGA(
        select_one.det_tournament(4),
        operator.OnePtBitCrossover(),.1,
        operator.DetBitFlip(),.7,
        eval,
        continuator.max_generations(self.NGENS)
    )

    #run algo on pop and print best individual
    sga(pop)
    print(pop.best())

Components

• EO (Population-based single-objective)
• MO (Trajectory-based single-objective)
• MOEO (Multi-objective)
• Encodings : Binary, Integer, Real, Permutation, Custom
• Genetic Operators : ...