(N+1)ES Simple Evolutionary Algorithm
(N+1)ES Simple Evolutionary Algorithm#

class sea#
(N+1)ES Simple Evolutionary Algorithm
Evolutionary strategies date back to the mid 1960s when P. Bienert, I. Rechenberg, and H.P. Schwefel at the Technical University of Berlin, Germany, developed the first bionicsinspired schemes for evolving optimal shapes of minimal drag bodies in a wind tunnel using Darwin’s evolution principle.
This c++ class represents the simplest evolutionary strategy, where a population of \( \lambda \) individuals at each generation produces one offspring by mutating its best individual uniformly at random within the bounds. Should the offspring be better than the worst individual in the population it will substitute it.
See also
Oliveto, Pietro S., Jun He, and Xin Yao. “Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results.” International Journal of Automation and Computing 4.3 (2007): 281293.
Note
The mutation is uniform within boxbounds. Hence, unbounded problems will produce undefined behaviours.
Warning
The algorithm is not suitable for multiobjective problems, nor for constrained or stochastic optimization
Public Types

typedef std::tuple<unsigned, unsigned long long, double, double, vector_double::size_type> log_line_type#
Single entry of the log (gen, fevals, best, improvement, mutations)

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

sea(unsigned gen = 1u, unsigned seed = pagmo::random_device::next())#
Constructor.
Constructs sea
 Parameters
gen – Number of generations to consider. Each generation will compute the objective function once
seed – seed used by the internal random number generator

population evolve(population) const#
Algorithm evolve method.
 Parameters
pop – population to be evolved
 Throws
std::invalid_argument – if the problem is multiobjective or constrained
 Returns
evolved population

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
1: will only print and log when the population is improved
>1: will print and log one line each
level
generations.
Example (verbosity 1):
Gen, is the generation number, Fevals the number of function evaluation used, Best is the best fitness function currently in the population, Improvement is the improvement made by the last mutation and Mutations is the number of mutated components of the decision vectorGen: Fevals: Best: Improvement: Mutations: 632 3797 1464.31 51.0203 1 633 3803 1463.23 13.4503 1 635 3815 1562.02 31.0434 3 667 4007 1481.6 24.1889 1 668 4013 1487.34 73.2677 2
 Parameters
level – verbosity level

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

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 std::string get_name() const#
Algorithm name.
One of the optional methods of any userdefined algorithm (UDA).
 Returns
a string containing the algorithm name

std::string get_extra_info() const#
Extra info.
One of the optional methods of any userdefined algorithm (UDA).
 Returns
a 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 sea::log_line_type containing: Gen, Fevals, Best, Improvement, Mutations as described in sea::set_verbosity Returns
an
std::vector
of sea::log_line_type containing the logged values Gen, Fevals, Best, Improvement, Mutations

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