IOH_random_local_search {IOHexperimenter}

IOHexperimenter-based wrapper

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)