self_adaptive_GA: IOHexperimenter-based wrapper

In IOHexperimenter: Benchmarking Part of the 'IOHprofiler'

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" )

IOHexperimenter documentation built on Sept. 1, 2020, 5:08 p.m.