OptimizerBatchLocalSearch class that implements a simple Local Search.
Local Search starts by determining the n_initial_points initial best points present in the Archive of the OptimInstance.
If fewer points than n_initial_points are present, additional initial_random_sample_size points sampled uniformly at random are evaluated and the best n_initial_points initial points are determined.
In each iteration, for each of the n_initial_points initial best points, neighbors_per_point neighbors are generated by local mutation.
Local mutation generates a neighbor by sampling a single parameter that is to be mutated and then proceeds as follows: Double parameters (paradox::p_dbl()) are mutated via Gaussian mutation (with a prior standardization to [0, 1] and retransformation after mutation).
Integer parameters (paradox::p_int()) undergo the same mutation but are rounded to the closest integer after mutation.
Categorical parameters (paradox::p_fct() and paradox::p_lgl()) are mutated via uniform mutation.
Note that parameters that are conditioned on (i.e., they are parents of a paradox::Condition, see the dependencies of the search space) are not mutated.
Dictionary
This Optimizer can be instantiated via the dictionary
mlr_optimizers or with the associated sugar function opt():
Parameters
n_initial_pointsinteger(1)
Size of the set of initial best points which are used as starting points for the Local Search. Default is10.initial_random_sample_sizeinteger(1)
Number of points that are sampled uniformly at random before the bestn_initial_pointsinitial points are determined, if fewer points thann_initial_pointsare present in the Archive of the OptimInstance. Default is100.neighbors_per_pointinteger(1)
Number of neighboring points to generate for each of then_initial_pointsbest starting points in each iteration. Default is100.mutation_sdnumeric(1)
Standard deviation used to create neighbors during mutation of numeric parameters on the standardized[0, 1]scale. Default is0.1.
Archive
The Archive holds the following additional column that is specific to the algorithm:
.point_id(integer(1))
The id (1, ..., n_initial_points) indicating from which of then_initial_pointsbest points the evaluated point was generated from.
Progress Bars
$optimize() supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress() to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress").
Super classes
bbotk::Optimizer -> bbotk::OptimizerBatch -> OptimizerBatchLocalSearch
Examples
search_space = domain = ps(x = p_dbl(lower = -1, upper = 1))
codomain = ps(y = p_dbl(tags = "minimize"))
objective_function = function(xs) {
list(y = as.numeric(xs)^2)
}
objective = ObjectiveRFun$new(
fun = objective_function,
domain = domain,
codomain = codomain)
instance = OptimInstanceBatchSingleCrit$new(
objective = objective,
search_space = search_space,
terminator = trm("evals", n_evals = 100))
# evaluate an initial sample of 10 points uniformly at random
# choose the best 3 points as the initial points
# for each of these points generate 10 neighbors
# repeat this process
optimizer = opt("local_search",
n_initial_points = 3,
initial_random_sample_size = 10,
neighbors_per_point = 10)
# modifies the instance by reference
optimizer$optimize(instance)
#> x x_domain y
#> <num> <list> <num>
#> 1: -0.001513127 <list[1]> 2.289553e-06
# returns best scoring evaluation
instance$result
#> x x_domain y
#> <num> <list> <num>
#> 1: -0.001513127 <list[1]> 2.289553e-06
# allows access of data.table of full path of all evaluations
as.data.table(instance$archive$data)
#> x y x_domain timestamp batch_nr .point_id
#> <num> <num> <list> <POSc> <int> <int>
#> 1: -0.159742753 2.551775e-02 <list[1]> 2024-11-08 08:28:06 1 NA
#> 2: -0.282035319 7.954392e-02 <list[1]> 2024-11-08 08:28:06 1 NA
#> 3: 0.360471830 1.299399e-01 <list[1]> 2024-11-08 08:28:06 1 NA
#> 4: -0.573702628 3.291347e-01 <list[1]> 2024-11-08 08:28:06 1 NA
#> 5: -0.018821961 3.542662e-04 <list[1]> 2024-11-08 08:28:06 1 NA
#> 6: 0.926881443 8.591092e-01 <list[1]> 2024-11-08 08:28:06 1 NA
#> 7: 0.893151477 7.977196e-01 <list[1]> 2024-11-08 08:28:06 1 NA
#> 8: -0.896230842 8.032297e-01 <list[1]> 2024-11-08 08:28:06 1 NA
#> 9: -0.409851406 1.679782e-01 <list[1]> 2024-11-08 08:28:06 1 NA
#> 10: 0.248380058 6.169265e-02 <list[1]> 2024-11-08 08:28:06 1 NA
#> 11: 0.044367130 1.968442e-03 <list[1]> 2024-11-08 08:28:06 2 1
#> 12: -0.001513127 2.289553e-06 <list[1]> 2024-11-08 08:28:06 2 1
#> 13: -0.127162798 1.617038e-02 <list[1]> 2024-11-08 08:28:06 2 1
#> 14: -0.022984094 5.282686e-04 <list[1]> 2024-11-08 08:28:06 2 1
#> 15: -0.398864827 1.590932e-01 <list[1]> 2024-11-08 08:28:06 2 1
#> 16: -0.046068272 2.122286e-03 <list[1]> 2024-11-08 08:28:06 2 1
#> 17: 0.342370982 1.172179e-01 <list[1]> 2024-11-08 08:28:06 2 1
#> 18: 0.272922608 7.448675e-02 <list[1]> 2024-11-08 08:28:06 2 1
#> 19: -0.429658091 1.846061e-01 <list[1]> 2024-11-08 08:28:06 2 1
#> 20: -0.035604171 1.267657e-03 <list[1]> 2024-11-08 08:28:06 2 1
#> 21: -0.069783331 4.869713e-03 <list[1]> 2024-11-08 08:28:06 2 2
#> 22: -0.363784264 1.323390e-01 <list[1]> 2024-11-08 08:28:06 2 2
#> 23: -0.011159547 1.245355e-04 <list[1]> 2024-11-08 08:28:06 2 2
#> 24: 0.029740669 8.845074e-04 <list[1]> 2024-11-08 08:28:06 2 2
#> 25: -0.187991993 3.534099e-02 <list[1]> 2024-11-08 08:28:06 2 2
#> 26: -0.074284940 5.518252e-03 <list[1]> 2024-11-08 08:28:06 2 2
#> 27: -0.692471195 4.795164e-01 <list[1]> 2024-11-08 08:28:06 2 2
#> 28: -0.239601261 5.740876e-02 <list[1]> 2024-11-08 08:28:06 2 2
#> 29: 0.077985842 6.081791e-03 <list[1]> 2024-11-08 08:28:06 2 2
#> 30: -0.140295110 1.968272e-02 <list[1]> 2024-11-08 08:28:06 2 2
#> 31: 0.030410879 9.248216e-04 <list[1]> 2024-11-08 08:28:06 2 3
#> 32: 0.308999637 9.548078e-02 <list[1]> 2024-11-08 08:28:06 2 3
#> 33: 0.435351301 1.895308e-01 <list[1]> 2024-11-08 08:28:06 2 3
#> 34: 0.457006694 2.088551e-01 <list[1]> 2024-11-08 08:28:06 2 3
#> 35: 0.045621198 2.081294e-03 <list[1]> 2024-11-08 08:28:06 2 3
#> 36: 0.343133166 1.177404e-01 <list[1]> 2024-11-08 08:28:06 2 3
#> 37: 0.099376008 9.875591e-03 <list[1]> 2024-11-08 08:28:06 2 3
#> 38: 0.296414382 8.786149e-02 <list[1]> 2024-11-08 08:28:06 2 3
#> 39: 0.130223904 1.695827e-02 <list[1]> 2024-11-08 08:28:06 2 3
#> 40: 0.204822816 4.195239e-02 <list[1]> 2024-11-08 08:28:06 2 3
#> 41: -0.142292139 2.024705e-02 <list[1]> 2024-11-08 08:28:06 3 1
#> 42: 0.080505567 6.481146e-03 <list[1]> 2024-11-08 08:28:06 3 1
#> 43: 0.379181819 1.437789e-01 <list[1]> 2024-11-08 08:28:06 3 1
#> 44: 0.372006467 1.383888e-01 <list[1]> 2024-11-08 08:28:06 3 1
#> 45: 0.165140076 2.727124e-02 <list[1]> 2024-11-08 08:28:06 3 1
#> 46: 0.033959116 1.153222e-03 <list[1]> 2024-11-08 08:28:06 3 1
#> 47: 0.084342708 7.113692e-03 <list[1]> 2024-11-08 08:28:06 3 1
#> 48: -0.245305539 6.017481e-02 <list[1]> 2024-11-08 08:28:06 3 1
#> 49: -0.172299939 2.968727e-02 <list[1]> 2024-11-08 08:28:06 3 1
#> 50: -0.163614478 2.676970e-02 <list[1]> 2024-11-08 08:28:06 3 1
#> 51: 0.033185269 1.101262e-03 <list[1]> 2024-11-08 08:28:06 3 2
#> 52: -0.267968019 7.180686e-02 <list[1]> 2024-11-08 08:28:06 3 2
#> 53: 0.111418436 1.241407e-02 <list[1]> 2024-11-08 08:28:06 3 2
#> 54: -0.079518051 6.323120e-03 <list[1]> 2024-11-08 08:28:06 3 2
#> 55: 0.099788630 9.957771e-03 <list[1]> 2024-11-08 08:28:06 3 2
#> 56: -0.260609203 6.791716e-02 <list[1]> 2024-11-08 08:28:06 3 2
#> 57: 0.349606121 1.222244e-01 <list[1]> 2024-11-08 08:28:06 3 2
#> 58: -0.162403385 2.637486e-02 <list[1]> 2024-11-08 08:28:06 3 2
#> 59: 0.130694906 1.708116e-02 <list[1]> 2024-11-08 08:28:06 3 2
#> 60: 0.010103175 1.020742e-04 <list[1]> 2024-11-08 08:28:06 3 2
#> 61: 0.153005961 2.341082e-02 <list[1]> 2024-11-08 08:28:06 3 3
#> 62: 0.373244179 1.393112e-01 <list[1]> 2024-11-08 08:28:06 3 3
#> 63: -0.235554463 5.548591e-02 <list[1]> 2024-11-08 08:28:06 3 3
#> 64: 0.044022126 1.937948e-03 <list[1]> 2024-11-08 08:28:06 3 3
#> 65: -0.214623212 4.606312e-02 <list[1]> 2024-11-08 08:28:06 3 3
#> 66: -0.069237414 4.793819e-03 <list[1]> 2024-11-08 08:28:06 3 3
#> 67: 0.400363140 1.602906e-01 <list[1]> 2024-11-08 08:28:06 3 3
#> 68: -0.046393243 2.152333e-03 <list[1]> 2024-11-08 08:28:06 3 3
#> 69: -0.229867543 5.283909e-02 <list[1]> 2024-11-08 08:28:06 3 3
#> 70: 0.056793802 3.225536e-03 <list[1]> 2024-11-08 08:28:06 3 3
#> 71: -0.430078169 1.849672e-01 <list[1]> 2024-11-08 08:28:06 4 1
#> 72: 0.158457044 2.510863e-02 <list[1]> 2024-11-08 08:28:06 4 1
#> 73: 0.036005937 1.296427e-03 <list[1]> 2024-11-08 08:28:06 4 1
#> 74: -0.024618599 6.060754e-04 <list[1]> 2024-11-08 08:28:06 4 1
#> 75: 0.083562841 6.982748e-03 <list[1]> 2024-11-08 08:28:06 4 1
#> 76: -0.235123790 5.528320e-02 <list[1]> 2024-11-08 08:28:06 4 1
#> 77: 0.137688515 1.895813e-02 <list[1]> 2024-11-08 08:28:06 4 1
#> 78: -0.302569192 9.154812e-02 <list[1]> 2024-11-08 08:28:06 4 1
#> 79: 0.120793137 1.459098e-02 <list[1]> 2024-11-08 08:28:06 4 1
#> 80: -0.159037421 2.529290e-02 <list[1]> 2024-11-08 08:28:06 4 1
#> 81: 0.016831489 2.832990e-04 <list[1]> 2024-11-08 08:28:06 4 2
#> 82: -0.183462202 3.365838e-02 <list[1]> 2024-11-08 08:28:06 4 2
#> 83: 0.014621865 2.137989e-04 <list[1]> 2024-11-08 08:28:06 4 2
#> 84: 0.111163605 1.235735e-02 <list[1]> 2024-11-08 08:28:06 4 2
#> 85: 0.050326959 2.532803e-03 <list[1]> 2024-11-08 08:28:06 4 2
#> 86: -0.017896682 3.202912e-04 <list[1]> 2024-11-08 08:28:06 4 2
#> 87: -0.500897760 2.508986e-01 <list[1]> 2024-11-08 08:28:06 4 2
#> 88: 0.384285475 1.476753e-01 <list[1]> 2024-11-08 08:28:06 4 2
#> 89: 0.063974974 4.092797e-03 <list[1]> 2024-11-08 08:28:06 4 2
#> 90: 0.396718366 1.573855e-01 <list[1]> 2024-11-08 08:28:06 4 2
#> 91: 0.004141410 1.715128e-05 <list[1]> 2024-11-08 08:28:06 4 3
#> 92: 0.162168377 2.629858e-02 <list[1]> 2024-11-08 08:28:06 4 3
#> 93: -0.097397100 9.486195e-03 <list[1]> 2024-11-08 08:28:06 4 3
#> 94: -0.282796497 7.997386e-02 <list[1]> 2024-11-08 08:28:06 4 3
#> 95: 0.032444437 1.052642e-03 <list[1]> 2024-11-08 08:28:06 4 3
#> 96: 0.238059682 5.667241e-02 <list[1]> 2024-11-08 08:28:06 4 3
#> 97: -0.412334177 1.700195e-01 <list[1]> 2024-11-08 08:28:06 4 3
#> 98: 0.385714496 1.487757e-01 <list[1]> 2024-11-08 08:28:06 4 3
#> 99: 0.245431637 6.023669e-02 <list[1]> 2024-11-08 08:28:06 4 3
#> 100: -0.099390131 9.878398e-03 <list[1]> 2024-11-08 08:28:06 4 3
#> x y x_domain timestamp batch_nr .point_id