Non dominated sorting genetic algorithm (NSGAII)
Non dominated sorting genetic algorithm (NSGAII)#

class nsga2#
Nondominated Sorting genetic algorithm II (NSGAII)
NSGAII is a solid multiobjective algorithm, widely used in many realworld applications. While today it can be considered as an outdated approach, nsga2 has still a great value, if not as a solid benchmark to test against. NSGAII generates offsprings using a specific type of crossover and mutation and then selects the next generation according to nondominatedsorting and crowding distance comparison.
The version implemented in pagmo can be applied to boxbounded multipleobjective optimization. It also deals with integer chromosomes treating the last
int_dim
entries in the decision vector as integers.See: Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGAII. IEEE transactions on evolutionary computation, 6(2), 182197.
Public Types

typedef std::tuple<unsigned, unsigned long long, vector_double> log_line_type#
Single entry of the log (gen, fevals, ideal_point)

typedef std::vector<log_line_type> log_type#
The log.
Public Functions

nsga2(unsigned gen = 1u, double cr = 0.95, double eta_c = 10., double m = 0.01, double eta_m = 50., unsigned seed = pagmo::random_device::next())#
Constructor.
Constructs the NSGA II user defined algorithm.
 Parameters
gen – [in] Number of generations to evolve.
cr – [in] Crossover probability.
eta_c – [in] Distribution index for crossover.
m – [in] Mutation probability.
eta_m – [in] Distribution index for mutation.
seed – seed used by the internal random number generator (default is random)
 Throws
std::invalid_argument – if
cr
is not \( \in [0,1[\),m
is not \( \in [0,1]\),eta_c
is not in [1,100[ oreta_m
is not in [1,100[.

population evolve(population) const#
Algorithm evolve method.
Evolves the population for the requested number of generations.
 Parameters
pop – population to be evolved
 Throws
std::invalid_argument – if pop.get_problem() is stochastic, single objective or has non linear constraints. If
int_dim
is larger than the problem dimension. If the population size is smaller than 5 or not a multiple of 4. Returns
evolved population

void set_seed(unsigned)#
Sets the seed.
 Parameters
seed – the seed controlling the algorithm stochastic behaviour

inline unsigned get_seed() const#
Gets the seed.
 Returns
the seed controlling the algorithm stochastic behaviour

inline void set_verbosity(unsigned level)#
Sets the algorithm verbosity.
Sets the verbosity level of the screen output and of the log returned by get_log().
level
can be:0: no verbosity
>0: will print and log one line each
level
generations.
Example (verbosity 1):
Gen, is the generation number, Fevals the number of function evaluation used. The ideal point of the current population follows cropped to its 5th component.Gen: Fevals: ideal1: ideal2: ideal3: 1 0 0.0257554 0.267768 0.974592 2 52 0.0257554 0.267768 0.908174 3 104 0.0257554 0.124483 0.822804 4 156 0.0130094 0.121889 0.650099 5 208 0.00182705 0.0987425 0.650099 6 260 0.0018169 0.0873995 0.509662 7 312 0.00154273 0.0873995 0.492973 8 364 0.00154273 0.0873995 0.471251 9 416 0.000379582 0.0873995 0.471251 10 468 0.000336743 0.0855247 0.432144
 Parameters
level – verbosity level

inline unsigned get_verbosity() const#
Gets the verbosity level.
 Returns
the verbosity level

void set_bfe(const bfe &b)#
Sets the batch function evaluation scheme.
 Parameters
b – batch function evaluation object

inline std::string get_name() const#
Algorithm name.
Returns the name of the algorithm.
 Returns
std::string
containing the algorithm name

std::string get_extra_info() const#
Extra info.
Returns extra information on the algorithm.
 Returns
an
std::string
containing extra info on the algorithm

inline const log_type &get_log() const#
Get log.
A log containing relevant quantities monitoring the last call to evolve. Each element of the returned
std::vector
is a nsga2::log_line_type containing: Gen, Fevals, ideal_point as described in nsga2::set_verbosity Returns
an
std::vector
of nsga2::log_line_type containing the logged values Gen, Fevals, ideal_point

typedef std::tuple<unsigned, unsigned long long, vector_double> log_line_type#