Evolving a population

Solving an optimization problem using an optimization algorithm is, in pagmo, described as evolving a population. In the scientific literature, an interesting discussion has developed over the past decades on whether evolution is or not a form of optimization. In pagmo we take the opposite standpoint and we regard optimization, of all types, as a form of evolution. Regardless on whether you will be using an SQP, an interior point optimizer or an evolutionary strategy solver, in pagmo you will always have to call a method called evolve() to improve over your initial solutions, i.e., your population.

After following the installation guide, you will be able to compile and run C++ pagmo programs. The following example shows the use with no multithreading of an algorithmic evolution:

#include <iostream>

#include <pagmo/algorithm.hpp>
#include <pagmo/algorithms/nsga2.hpp>
#include <pagmo/population.hpp>
#include <pagmo/problem.hpp>
#include <pagmo/problems/dtlz.hpp>

using namespace pagmo;

int main()
{
    // 1 - Instantiate a pagmo problem constructing it from a UDP
    // (user defined problem).
    problem prob{dtlz(1, 10, 2)};

    // 2 - Instantiate a pagmo algorithm
    algorithm algo{nsga2(100)};

    // 3 - Instantiate a population
    population pop{prob, 24};

    // 4 - Evolve the population
    pop = algo.evolve(pop);

    // 5 - Output the population
    std::cout << "The population: \n" << pop;
}

Place it into a nsga2.cpp text file and compile it (for example) with:

$ g++ -O2 -DNDEBUG -std=c++17 nsga2.cpp -ltbb -lboost_serialization