pyparadiseo 0.2.1

Python of the ParadisEO framework for heuristic optimization

Table of Contents

• Installation
• Usage
• Components

Installation

You can install pyParadiseo with pip

pip install pyparadiseo

Getting Started

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

# problem dependent
from pyparadiseo import Pop
from pyparadiseo.evaluator import FitnessEval,PopLoopEval
# encoding dependent
from pyparadiseo.initializer import BinaryInit
from pyparadiseo.operator import OnePtBitCrossover,DetBitFlip
# independent
from pyparadiseo.eo import algo,selector,continuator

import numpy as np

if __name__ == "__main__":
    #make pyparadiseo evaluator from python function
    eval = FitnessEval(lambda sol: np.count_nonzero(sol))
    #generate and evaluate population
    pop = Pop(25, BinaryInit(20))
    PopLoopEval(eval)(pop,pop)
    #assemble simple GA
    sga = algo.SGA(
        selector.DetTournamentSelect(),
        OnePtBitCrossover(),.2,
        DetBitFlip(),.6,
        eval,
        continuator.GenContinue(100)
    )
    #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 : ...