name large_stringlengths 3 40 | real_name large_stringlengths 0 176 | optimization_class large_stringclasses 5
values | suite large_stringclasses 14
values | application large_stringclasses 7
values | dim int64 1 47.2k | num_objectives int64 1 9 | num_constraints int64 0 200 | input_type large_stringclasses 3
values | num_integer_variables int64 0 36 | num_categorical_variables int64 0 225 | scalable bool 2
classes | in_paper_benchmark bool 2
classes | source large_stringlengths 0 500 | module large_stringclasses 109
values | paper_name large_stringlengths 0 29 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AIG_adder | EPFL AIG Logic-Synthesis Sequence Optimization (128-bit Adder, adder) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_arbiter | EPFL AIG Logic-Synthesis Sequence Optimization (Round-Robin Arbiter, arbiter) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_bar | EPFL AIG Logic-Synthesis Sequence Optimization (Barrel Shifter, bar) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_cavlc | EPFL AIG Logic-Synthesis Sequence Optimization (CAVLC Coding Block, cavlc) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_ctrl | EPFL AIG Logic-Synthesis Sequence Optimization (ALU Control Unit, ctrl) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_dec | EPFL AIG Logic-Synthesis Sequence Optimization (128-bit Decoder, dec) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_div | EPFL AIG Logic-Synthesis Sequence Optimization (128-bit Divisor, div) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_hyp | EPFL AIG Logic-Synthesis Sequence Optimization (Hypotenuse, hyp) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_i2c | EPFL AIG Logic-Synthesis Sequence Optimization (I2C Controller, i2c) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_int2float | EPFL AIG Logic-Synthesis Sequence Optimization (Integer-to-Float Converter, int2float) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_log2 | EPFL AIG Logic-Synthesis Sequence Optimization (Binary Logarithm, log2) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_max | EPFL AIG Logic-Synthesis Sequence Optimization (Max of Four 128-bit Words, max) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_mem_ctrl | EPFL AIG Logic-Synthesis Sequence Optimization (Memory Controller, mem_ctrl) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_multiplier | EPFL AIG Logic-Synthesis Sequence Optimization (64-bit Multiplier, multiplier) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_priority | EPFL AIG Logic-Synthesis Sequence Optimization (Priority Encoder, priority) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_router | EPFL AIG Logic-Synthesis Sequence Optimization (Lookahead Router, router) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_sin | EPFL AIG Logic-Synthesis Sequence Optimization (Sine, sin) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_sqrt | EPFL AIG Logic-Synthesis Sequence Optimization (Square Root, sqrt) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_square | EPFL AIG Logic-Synthesis Sequence Optimization (Squarer, square) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
AIG_voter | EPFL AIG Logic-Synthesis Sequence Optimization (Majority Voter, voter) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 56 | 1 | 0 | discrete | 36 | 20 | false | false | K. Dreczkowski, A. Grosnit, H. Bou-Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks), 2023. arXiv:2306.09803. https://github.com/huawei-noah/HEBO/tree/master/MCBO A. Grosnit, C. Malherbe, R. Tutunov,... | bocode.opt_problems.engineering.EPFLSeqOpt | |
Ackley53 | Ackley (53-D mixed binary/continuous, Wan et al.) | SO-Mixed-Variable | Mixed-variable synthetic | Synthetic | 53 | 1 | 0 | mixed | 0 | 50 | false | true | X. Wan, V. Nguyen, H. Ha, B. Ru, C. Lu, M. A. Osborne. Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces. Proceedings of the 38th International Conference on Machine Learning, PMLR 139:10663-10674, 2021. https://github.com/xingchenwan/Casmopolitan S. Surjanovic,... | bocode.opt_problems.synthetic_mixed.Ackley53 | Ackley53 |
Ackley5Mixed | Ackley (5-D mixed-variable) | SO-Mixed-Variable | Mixed-variable synthetic | Synthetic | 5 | 1 | 0 | mixed | 0 | 2 | false | true | D. H. Ackley. A Connectionist Machine for Genetic Hillclimbing. Kluwer Academic Publishers, 1987 (the Ackley function). Mixed-variable sign-flip construction after the mixed-variable BO benchmark suites; see B. Ru et al., ICML 2020. | bocode.opt_problems.synthetic_mixed.Ackley5Mixed | Ackley5Mixed |
AckleyCat | Ackley (20-D categorical) | SO-Mixed-Variable | Mixed-variable synthetic | Synthetic | 20 | 1 | 0 | discrete | 0 | 20 | false | true | D. H. Ackley. A Connectionist Machine for Genetic Hillclimbing. Kluwer Academic Publishers, 1987 (the Ackley function). S. Surjanovic, D. Bingham. Virtual Library of Simulation Experiments: Test Functions and Datasets — Ackley Function. https://www.sfu.ca/~ssurjano/ackley.html K. Dreczkowski, A. Grosnit, H. Bou-Ammar. ... | bocode.opt_problems.synthetic_mixed.AckleyCat | AckleyCat |
AgNP | Silver-Nanoparticle (AgNP) Flow Synthesis Optimization | SO-Unconstrained | PV-Lab materials | Materials | 5 | 1 | 0 | continuous | 0 | 0 | false | true | F. Mekki-Berrada, Z. Ren, T. Huang, et al. Two-step machine learning enables optimized nanoparticle synthesis. npj Computational Materials 7:55, 2021. Dataset via the PV-Lab benchmarking suite: https://github.com/PV-Lab/Benchmarking | bocode.opt_problems.materials.AgNP | AgNP |
Allison | Allison Analytical-Target-Cascading Test Problem | SO-Unconstrained | Engineering (standalone) | Engineering | 3 | 1 | 0 | continuous | 0 | 0 | false | true | J. T. Allison. Complex system optimization: a review of analytical target cascading, collaborative optimization, and other formulations. M.S. thesis, University of Michigan, 2004. P. Norheim, minimdo (thesis_allison application). https://github.com/norheim/minimdo | bocode.opt_problems.engineering.Allison | Allison |
AntPolicySearchProblem | MuJoCo Ant-v5 — Linear-Policy Search | SO-Unconstrained | MuJoCo control | Control | 840 | 1 | 0 | continuous | 0 | 0 | false | true | bocode.opt_problems.control.mujoco.MujocoFuncs | AntPolicy | |
AntProblem | MuJoCo Ant-v5 — Constant-Action Control | SO-Unconstrained | MuJoCo control | Control | 8 | 1 | 0 | continuous | 0 | 0 | false | true | bocode.opt_problems.control.mujoco.MujocoFuncs | AntProblem | |
AntennaArray200 | Thinned + Phased Linear Antenna Array (peak sidelobe level, Haupt 1994) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 200 | 1 | 0 | mixed | 0 | 100 | false | false | R. L. Haupt. Thinned arrays using genetic algorithms. IEEE Transactions on Antennas and Propagation 42(7):993-999, 1994. | bocode.opt_problems.engineering.AntennaArray | |
AutoAM | Autonomous Additive Manufacturing (3D-printer process optimization) | SO-Unconstrained | PV-Lab materials | Materials | 4 | 1 | 0 | continuous | 0 | 0 | false | true | J. R. Deneault, J. Chang, J. Myung, et al. Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer. MRS Bulletin 46:566-575, 2021. Dataset via the PV-Lab benchmarking suite: https://github.com/PV-Lab/Benchmarking | bocode.opt_problems.materials.AutoAM | AutoAM |
Bayesmark_DT_breast | Bayesmark DT tuning on the breast dataset (6 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 6 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_DT_breast |
Bayesmark_DT_digits | Bayesmark DT tuning on the digits dataset (6 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 6 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_DT_digits |
Bayesmark_DT_iris | Bayesmark DT tuning on the iris dataset (6 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 6 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_DT_iris |
Bayesmark_DT_wine | Bayesmark DT tuning on the wine dataset (6 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 6 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_DT_wine |
Bayesmark_MLPadam_breast | Bayesmark MLP-adam tuning on the breast dataset (9 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 9 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_MLPadam_breast |
Bayesmark_MLPadam_digits | Bayesmark MLP-adam tuning on the digits dataset (9 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 9 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_MLPadam_digits |
Bayesmark_MLPadam_iris | Bayesmark MLP-adam tuning on the iris dataset (9 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 9 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_MLPadam_iris |
Bayesmark_MLPadam_wine | Bayesmark MLP-adam tuning on the wine dataset (9 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 9 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_MLPadam_wine |
Bayesmark_MLPsgd_breast | Bayesmark MLP-sgd tuning on the breast dataset (8 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 8 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_MLPsgd_breast |
Bayesmark_MLPsgd_digits | Bayesmark MLP-sgd tuning on the digits dataset (8 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 8 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_MLPsgd_digits |
Bayesmark_MLPsgd_iris | Bayesmark MLP-sgd tuning on the iris dataset (8 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 8 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_MLPsgd_iris |
Bayesmark_MLPsgd_wine | Bayesmark MLP-sgd tuning on the wine dataset (8 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 8 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_MLPsgd_wine |
Bayesmark_RF_breast | Bayesmark RF tuning on the breast dataset (6 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 6 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_RF_breast |
Bayesmark_RF_digits | Bayesmark RF tuning on the digits dataset (6 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 6 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_RF_digits |
Bayesmark_RF_iris | Bayesmark RF tuning on the iris dataset (6 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 6 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_RF_iris |
Bayesmark_RF_wine | Bayesmark RF tuning on the wine dataset (6 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 6 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_RF_wine |
Bayesmark_SVM_breast | Bayesmark SVM tuning on the breast dataset (3 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 3 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_SVM_breast |
Bayesmark_SVM_digits | Bayesmark SVM tuning on the digits dataset (3 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 3 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_SVM_digits |
Bayesmark_SVM_iris | Bayesmark SVM tuning on the iris dataset (3 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 3 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_SVM_iris |
Bayesmark_SVM_wine | Bayesmark SVM tuning on the wine dataset (3 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 3 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_SVM_wine |
Bayesmark_ada_breast | Bayesmark ada tuning on the breast dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_ada_breast |
Bayesmark_ada_digits | Bayesmark ada tuning on the digits dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_ada_digits |
Bayesmark_ada_iris | Bayesmark ada tuning on the iris dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_ada_iris |
Bayesmark_ada_wine | Bayesmark ada tuning on the wine dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_ada_wine |
Bayesmark_kNN_breast | Bayesmark kNN tuning on the breast dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_kNN_breast |
Bayesmark_kNN_digits | Bayesmark kNN tuning on the digits dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_kNN_digits |
Bayesmark_kNN_iris | Bayesmark kNN tuning on the iris dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_kNN_iris |
Bayesmark_kNN_wine | Bayesmark kNN tuning on the wine dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_kNN_wine |
Bayesmark_lasso_breast | Bayesmark lasso tuning on the breast dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_lasso_breast |
Bayesmark_lasso_digits | Bayesmark lasso tuning on the digits dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_lasso_digits |
Bayesmark_lasso_iris | Bayesmark lasso tuning on the iris dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_lasso_iris |
Bayesmark_lasso_wine | Bayesmark lasso tuning on the wine dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_lasso_wine |
Bayesmark_linear_breast | Bayesmark linear tuning on the breast dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_linear_breast |
Bayesmark_linear_digits | Bayesmark linear tuning on the digits dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_linear_digits |
Bayesmark_linear_iris | Bayesmark linear tuning on the iris dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_linear_iris |
Bayesmark_linear_wine | Bayesmark linear tuning on the wine dataset (2 hyperparameters), CV accuracy | SO-Unconstrained | HPO (LassoBench / HPO-B) | Hyperparameter Optimization | 2 | 1 | 0 | continuous | 0 | 0 | false | true | Uber. Bayesmark: benchmark framework for Bayesian optimization. https://github.com/uber/bayesmark S. Müller, M. Feurer, N. Hollmann, F. Hutter. PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023, arXiv:2305.17535. https://github.com/automl/PFNs4BO | bocode.opt_problems.hpo.Bayesmark | Bayesmark_linear_wine |
BikeBench64 | BikeBench Parametric Bicycle Design (mass + 34 constraints) | SO-Mixed-Variable | Engineering (standalone) | Engineering | 64 | 1 | 0 | mixed | 7 | 9 | false | false | L. Regenwetter, C. Y. Weaver, F. Ahmed, et al. BikeBench: A Benchmark for Engineering Design Generative Models. arXiv:2508.00830, 2025. https://github.com/Lyleregenwetter/BikeBench | bocode.opt_problems.engineering.BikeBench | |
Borehole | Borehole Function (water flow rate through a borehole) | SO-Unconstrained | Engineering (standalone) | Engineering | 8 | 1 | 0 | continuous | 0 | 0 | false | true | Surjanovic & Bingham file the borehole function under *Emulation & Prediction* test functions, not optimization test problems (https://www.sfu.ca/~ssurjano/emulat.html), and GP+ likewise uses it only as a regression testbed. The tie-breakers, in order: 1. The one use of borehole as a Bayesian-optimization *objective* t... | bocode.opt_problems.engineering.Borehole | Borehole |
BotorchCarSideImpact | Car Side Impact Design (4-objective, BoTorch formulation) | MO-Unconstrained | Engineering (standalone) | Engineering | 7 | 4 | 0 | continuous | 0 | 0 | false | false | H. Jain and K. Deb. An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II. IEEE Transactions on Evolutionary Computation 18(4):602-622, 2014. R. Tanabe and H. Ishibuchi. An easy-to-use real-world multi-objective optimization problem suite. Applied Soft ... | bocode.opt_problems.engineering.BotorchCarSideImpact | |
BraninCategorical | Branin (categorical) | SO-Mixed-Variable | Mixed-variable synthetic | Synthetic | 3 | 1 | 0 | mixed | 0 | 1 | false | true | F. H. Branin. Widely convergent method for finding multiple solutions of simultaneous nonlinear equations. IBM Journal of Research and Development 16(5):504-522, 1972 (the Branin function). Additive-offset categorical construction after the mixed-variable synthetic suites; see B. Ru et al., ICML 2020. | bocode.opt_problems.synthetic_mixed.BraninCategorical | BraninCategorical |
BraninLVGP | Branin (LVGP mixed quantitative/qualitative) | SO-Mixed-Variable | Mixed-variable synthetic | Synthetic | 2 | 1 | 0 | mixed | 0 | 1 | false | true | Y. Zhang, D. W. Apley, W. Chen. Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables. Scientific Reports 10:4924, 2020 (arXiv:1910.01688). | bocode.opt_problems.synthetic_mixed.BraninLVGP | BraninLVGP |
CEC2020_p1 | Heat Exchanger Network Design (case 1) | SO-Constrained | CEC2020 RW-Constrained | Engineering | 9 | 1 | 8 | continuous | 0 | 0 | false | false | CEC2020 Problem 1 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | |
CEC2020_p10 | Process Flow Sheeting Problem | SO-Constrained | CEC2020 RW-Constrained | Engineering | 3 | 1 | 3 | continuous | 0 | 0 | false | true | CEC2020 Problem 10 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | CEC2020_p10 |
CEC2020_p11 | Two-Reactor Problem | SO-Constrained | CEC2020 RW-Constrained | Engineering | 7 | 1 | 8 | continuous | 0 | 0 | false | false | CEC2020 Problem 11 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | |
CEC2020_p11_PS | Two-Reactor Problem (penalty-scalarized, SO-unconstrained) | SO-Unconstrained | CEC2020 RW-Constrained | Engineering | 7 | 1 | 0 | continuous | 0 | 0 | false | true | Penalty-scalarized wrapper of CEC2020_p11: equal-weight mean of all objectives minus total positive constraint violation. | bocode.opt_problems.scalarized | CEC2020_p11_PS |
CEC2020_p12 | Process Synthesis Problem (7-variable) | SO-Constrained | CEC2020 RW-Constrained | Engineering | 7 | 1 | 9 | continuous | 0 | 0 | false | true | CEC2020 Problem 12 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | CEC2020_p12 |
CEC2020_p13 | Process Design Problem | SO-Constrained | CEC2020 RW-Constrained | Engineering | 5 | 1 | 3 | continuous | 0 | 0 | false | true | CEC2020 Problem 13 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | CEC2020_p13 |
CEC2020_p14 | Multi-Product Batch Plant | SO-Constrained | CEC2020 RW-Constrained | Engineering | 10 | 1 | 10 | continuous | 0 | 0 | false | true | CEC2020 Problem 14 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | CEC2020_p14 |
CEC2020_p15 | Weight Minimization of a Speed Reducer | SO-Constrained | CEC2020 RW-Constrained | Engineering | 7 | 1 | 11 | continuous | 0 | 0 | false | true | CEC2020 Problem 15 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | CEC2020_p15 |
CEC2020_p16 | Optimal Design of Industrial Refrigeration System | SO-Constrained | CEC2020 RW-Constrained | Engineering | 14 | 1 | 15 | continuous | 0 | 0 | false | true | CEC2020 Problem 16 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | CEC2020_p16 |
CEC2020_p17 | Tension/Compression Spring Design (case 1) | SO-Constrained | CEC2020 RW-Constrained | Engineering | 3 | 1 | 4 | continuous | 0 | 0 | false | true | CEC2020 Problem 17 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | CEC2020_p17 |
CEC2020_p18 | Pressure Vessel Design | SO-Constrained | CEC2020 RW-Constrained | Engineering | 4 | 1 | 4 | continuous | 0 | 0 | false | true | CEC2020 Problem 18 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | CEC2020_p18 |
CEC2020_p19 | Welded Beam Design | SO-Constrained | CEC2020 RW-Constrained | Engineering | 4 | 1 | 5 | continuous | 0 | 0 | false | true | CEC2020 Problem 19 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | CEC2020_p19 |
CEC2020_p1_PS | Heat Exchanger Network Design (case 1) (penalty-scalarized, SO-unconstrained) | SO-Unconstrained | CEC2020 RW-Constrained | Engineering | 9 | 1 | 0 | continuous | 0 | 0 | false | true | Penalty-scalarized wrapper of CEC2020_p1: equal-weight mean of all objectives minus total positive constraint violation. | bocode.opt_problems.scalarized | CEC2020_p1_PS |
CEC2020_p2 | Heat Exchanger Network Design (case 2) | SO-Constrained | CEC2020 RW-Constrained | Engineering | 11 | 1 | 9 | continuous | 0 | 0 | false | false | CEC2020 Problem 2 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | |
CEC2020_p20 | Three-Bar Truss Design | SO-Constrained | CEC2020 RW-Constrained | Engineering | 2 | 1 | 3 | continuous | 0 | 0 | false | true | CEC2020 Problem 20 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | CEC2020_p20 |
CEC2020_p21 | Multiple Disk Clutch Brake Design | SO-Constrained | CEC2020 RW-Constrained | Engineering | 5 | 1 | 8 | continuous | 0 | 0 | false | true | CEC2020 Problem 21 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p21 |
CEC2020_p22 | Planetary Gear Train Design Optimization | SO-Constrained | CEC2020 RW-Constrained | Engineering | 9 | 1 | 11 | continuous | 0 | 0 | false | true | CEC2020 Problem 22 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p22 |
CEC2020_p23 | Step-Cone Pulley Problem | SO-Constrained | CEC2020 RW-Constrained | Engineering | 5 | 1 | 11 | continuous | 0 | 0 | false | true | CEC2020 Problem 23 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p23 |
CEC2020_p24 | Robot Gripper Problem | SO-Constrained | CEC2020 RW-Constrained | Engineering | 7 | 1 | 7 | continuous | 0 | 0 | false | true | CEC2020 Problem 24 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p24 |
CEC2020_p25 | Hydro-Static Thrust Bearing Design | SO-Constrained | CEC2020 RW-Constrained | Engineering | 4 | 1 | 7 | continuous | 0 | 0 | false | true | CEC2020 Problem 25 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p25 |
CEC2020_p26 | Four-Stage Gear Box Problem | SO-Constrained | CEC2020 RW-Constrained | Engineering | 22 | 1 | 86 | continuous | 0 | 0 | false | true | CEC2020 Problem 26 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p26 |
CEC2020_p27 | 10-Bar Truss Design | SO-Constrained | CEC2020 RW-Constrained | Engineering | 10 | 1 | 3 | continuous | 0 | 0 | false | true | CEC2020 Problem 27 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p27 |
CEC2020_p28 | Rolling Element Bearing Design | SO-Constrained | CEC2020 RW-Constrained | Engineering | 10 | 1 | 9 | continuous | 0 | 0 | false | true | CEC2020 Problem 28 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p28 |
CEC2020_p29 | Gas Transmission Compressor Design (GTCD) | SO-Constrained | CEC2020 RW-Constrained | Engineering | 4 | 1 | 1 | continuous | 0 | 0 | false | true | CEC2020 Problem 29 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p29 |
CEC2020_p2_PS | Heat Exchanger Network Design (case 2) (penalty-scalarized, SO-unconstrained) | SO-Unconstrained | CEC2020 RW-Constrained | Engineering | 11 | 1 | 0 | continuous | 0 | 0 | false | true | Penalty-scalarized wrapper of CEC2020_p2: equal-weight mean of all objectives minus total positive constraint violation. | bocode.opt_problems.scalarized | CEC2020_p2_PS |
CEC2020_p3 | Optimal Operation of Alkylation Unit | SO-Constrained | CEC2020 RW-Constrained | Engineering | 7 | 1 | 14 | continuous | 0 | 0 | false | false | CEC2020 Problem 3 | bocode.opt_problems.cec2020_rw.CEC2020_p1_20 | |
CEC2020_p30 | Tension/Compression Spring Design (case 2) | SO-Constrained | CEC2020 RW-Constrained | Engineering | 3 | 1 | 8 | continuous | 0 | 0 | false | true | CEC2020 Problem 30 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p30 |
CEC2020_p31 | Gear Train Design (Sandgren) | SO-Unconstrained | CEC2020 RW-Constrained | Engineering | 4 | 1 | 0 | continuous | 0 | 0 | false | false | CEC2020 Problem 31 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | |
CEC2020_p32 | Himmelblau's Function | SO-Constrained | CEC2020 RW-Constrained | Engineering | 5 | 1 | 6 | continuous | 0 | 0 | false | true | CEC2020 Problem 32 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p32 |
CEC2020_p33 | Topology Optimization | SO-Constrained | CEC2020 RW-Constrained | Engineering | 30 | 1 | 30 | continuous | 0 | 0 | false | true | CEC2020 Problem 33 | bocode.opt_problems.cec2020_rw.CEC2020_p21_33 | CEC2020_p33 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.