Abstract CMA-ES is the gold standard for continuous black-box optimization, but it has diminishing returns: after convergence, additional CMA-ES trials provide little improvement. This sampler addresses that by splitting the trial budget into three phases:
Sobol QMC (8 trials) — quasi-random space-filling initialization CMA-ES (132 trials) — covariance matrix adaptation for main optimization Quasi-random Gaussian refinement (60 trials) — targeted local search around the best point using Sobol-based perturbation vectors with exponentially decaying scale The refinement phase uses quasi-random Sobol sequences transformed via inverse CDF to generate Gaussian-distributed perturbation vectors.
Abstract The blackbox optimization benchmarking (bbob) test suite comprises 24 noiseless single-objective test functions. BBOB is one of the most widely used test suites to evaluate and compare the performance of blackbox optimization algorithms. Each benchmark function is provided in dimensions [2, 3, 5, 10, 20, 40] with 110 instances.
APIs class Problem(function_id: int, dimension: int, instance_id: int = 1) function_id: ID of the bbob benchmark function to use. It must be in the range of [1, 24].
Abstract The bbob-biobj test suite was created by combining existing 55 noiseless single-objective test functions. bbob-biobj (and its extension, bbob-biobj-ext) has in total of 92 (= original 55 + additional 37) bi-objective functions. Each benchmark function is provided in dimensions [2, 3, 5, 10, 20, 40] with 15 instances. In this package, all the 92 functions are available. Please refer to the paper for details about each benchmark function.
APIs class Problem(function_id: int, dimension: int, instance_id: int = 1) function_id: ID of the bbob benchmark function to use.
Abstract The blackbox optimization benchmarking biobj-mixed-integer (bbob-biobj-mixint) test suite comprises 92 noiseless mixed-integer bi-objective test functions. Each benchmark function is provided in dimensions [5, 10, 20, 40, 80, 160] with 15 instances. Please refer to the paper for details about each benchmark function.
APIs class Problem(function_id: int, dimension: int, instance_id: int = 1) function_id: ID of the bbob benchmark function to use. It must be in the range of [1, 92].
Abstract The blackbox optimization benchmarking largescale (bbob-largescale) test suite comprises high-dimensional 24 noiseless single-objective test functions. Each benchmark function is provided in dimensions [20, 40, 80, 160, 320, 640] with 15 instances. Please refer to the paper for details about each benchmark function.
APIs class Problem(function_id: int, dimension: int, instance_id: int = 1) function_id: ID of the bbob benchmark function to use. It must be in the range of [1, 24].
Abstract The blackbox optimization benchmarking mixed-integer (bbob-mixint) test suite comprises 24 noiseless mixed-integer single-objective test functions. Each benchmark function is provided in dimensions [5, 10, 20, 40, 80, 160] with 15 instances. Please refer to the paper for details about each benchmark function.
APIs class Problem(function_id: int, dimension: int, instance_id: int = 1) function_id: ID of the bbob benchmark function to use. It must be in the range of [1, 24].
Abstract The blackbox optimization benchmarking noisy (bbob-noisy) test suite comprises 30 noisy test functions. Each benchmark function is provided in dimensions [2, 3, 5, 10, 20, 40] with 15 instances. Please refer to the paper for details about each benchmark function.
APIs class Problem(function_id: int, dimension: int, instance_id: int = 1) function_id: ID of the bbob benchmark function to use. It must be in the range of [101, 130]. dimension: Dimension of the benchmark function.
Abstract This package provides a wrapper of the COCO experiments libarary’s bbob-constrained test suite.
APIs class Problem(function_id: int, dimension: int, instance_id: int = 1) function_id: ID of the bbob-constrained benchmark function to use. It must be in the range of [1, 54]. dimension: Dimension of the benchmark function. It must be in [2, 3, 5, 10, 20, 40]. instance_id: ID of the instance of the benchmark function. It must be in the range of [1, 15].