random_local_search: IOHexperimenter-based wrapper

In IOHexperimenter: Benchmarking Part of the 'IOHprofiler'

Description

For easier use with the IOHexperimenter

The simplest stochastic optimization algorithm for discrete problems. A randomly chosen position in the solution vector is perturbated in each iteration. Only improvements are accepted after perturbation.

Usage

IOH_random_local_search(IOHproblem, budget = NULL)

random_local_search(dimension, obj_func, target_hit = function() {    
  FALSE }, budget = NULL)

Arguments

IOHproblem	An IOHproblem object
budget	integer, maximal allowable number of function evaluations
dimension	Dimension of search space
obj_func	The evaluation function
target_hit	Optional, function which enables early stopping if a target value is reached

Examples

benchmark_algorithm(IOH_random_local_search, data.dir = NULL)

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