IOH_self_adaptive_GA {IOHexperimenter} | R Documentation |
IOHexperimenter-based wrapper
Description
For easier use with the IOHexperimenter
A genetic algorithm that controls the mutation rate (strength) using the so-called self-adaptation mechanism: the mutation rate is firstly perturbated and then the resulting value is taken to mutate Lambda solution vector. The best solution is selected along with its mutation rate.
Usage
IOH_self_adaptive_GA(IOHproblem, lambda_ = 1, budget = NULL)
self_adaptive_GA(dimension, obj_func, lambda_ = 10, budget = NULL,
set_parameters = NULL, target_hit = function() { FALSE })
Arguments
IOHproblem |
An IOHproblem object |
lambda_ |
The size of the offspring |
budget |
How many times the objective function can be evaluated |
dimension |
Dimension of search space |
obj_func |
The evaluation function |
set_parameters |
Function to call to store the value of the registered parameters |
target_hit |
Optional, function which enables early stopping if a target value is reached |
Examples
one_comma_two_EA <- function(IOHproblem) { IOH_self_adaptive_GA(IOHproblem, lambda_=2) }
benchmark_algorithm(one_comma_two_EA, params.track = "Mutation_rate",
algorithm.name = "one_comma_two_EA", data.dir = NULL,
algorithm.info = "Using one_comma_two_EA with specific parameter" )
[Package IOHexperimenter version 0.1.4 Index]