two_rate_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 2-rate self-adaptation mechanism: the mutation rate is based on a parameter r. For each generation, half offspring are generated by mutation rate 2r/dim, and half by r/2dim. r that the best offspring has been created with will be inherited by probability 3/4, the other by 1/4.

Usage

IOH_two_rate_GA(IOHproblem, lambda_ = 1, budget = NULL)

two_rate_GA(dimension, obj_func, target_hit = function() {     FALSE },
  lambda_ = 2, budget = NULL, set_parameters = NULL)

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
target_hit	Optional, function which enables early stopping if a target value is reached
set_parameters	Function to call to store the value of the registered parameters

Examples

benchmark_algorithm(IOH_two_rate_GA)

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