Submit features and corrections on GitHub with pull requests / issues, or by email: koen.van.der.blom@cwi.nl
| name | domain | objectives | variable type | constraints | reference | implementation | textual description |
|---|---|---|---|---|---|---|---|
| Exploratory Landscape Analysis (ELA) | black-box | 1 | continuous | unconstrained or box-constrained | https://doi.org/10.1145/2001576.2001690 | https://github.com/Reiyan/pflacco | Exploratory Landscape Analysis (ELA) features |
| Cell Mapping Features | black-box | 1 | continuous | unconstrained or box-constrained | https://doi.org/10.1007/978-3-319-07494-8_9 | https://github.com/Reiyan/pflacco | Cell Mapping Techniques for Exploratory Landscape Analysis |
| Dispersion Features | black-box | 1 | continuous | unconstrained or box-constrained | https://doi.org/10.1145/1143997.1144085 | https://github.com/Reiyan/pflacco | The Dispersion Metric |
| Information Content-Based Features | black-box | 1 | continuous | unconstrained or box-constrained | https://doi.org/10.1109/TEVC.2014.2302006 | https://github.com/Reiyan/pflacco | Exploratory Landscape Analysis of Continuous Space Optimization Problems Using Information Content. Information Content of Fitness Sequences (ICoFiS). |
| Nearest Better Features | black-box | 1 | continuous | unconstrained or box-constrained | https://doi.org/10.1145/2739480.2754642 | https://github.com/Reiyan/pflacco | The Nearest-Better Features - also called Nearest-Better Clustering (NBC) Features. Features for detecting funnel structures. |
| Barrier Tree Features | black-box | 1 | continuous | unconstrained or box-constrained | https://doi.org/10.1007/978-3-030-25147-5_7 | https://github.com/kerschke/flacco | Following generalized cell mapping, barrier trees can be used to characterize the problem structure. |
| Principal Components Features | black-box | 1 | continuous | unconstrained or box-constrained | https://doi.org/10.1007/978-3-030-25147-5_7 | https://github.com/Reiyan/pflacco | Describe the variable scaling of continuous problems by applying a Principal Component Analysis. |
| Linear model features | black-box | 1 | continuous | unconstrained or box-constrained | https://doi.org/10.1007/978-3-030-25147-5_7 | https://github.com/kerschke/flacco | Following generalized cell mapping, barrier trees can be used to characterize the problem structure. |
| Multi-objective Combinatorial Problem Features | black-box | 2+ | binary/combinatorial | unconstrained | https://doi.org/10.1162/evco_a_00193 | ? | Features describing Multi-objective Combinatorial Problems. |
| Local Optima Networks (LON) | black-box | 1 | combinatorial | unconstrained | https://doi.org/10.1007/978-3-642-41888-4_9 | ? | Local Optima Networks (LON): A network-based model of combinatorial landscapes. |
| DynamoRep | black-box | 1 | continuous | unconstrained | https://doi.org/10.1162/evco_a_00370 | ? | DynamoRep computes basic descriptive statistics such as min, max, mean, std over x and y values to capture algorithm-problem interaction in single-objective continuous optimization. |
| Opt2Vec | black-box | 1 | continuous | unconstrained | https://doi.org/10.1016/j.ins.2024.121134 | ? | A continuous optimization problem representation based on algorithm behavior. |
| DoE2Vec | black-box | 1 | continuous | unconstrained | https://doi.org/10.1145/3583133.3590609 | https://github.com/nikivanstein/doe2vec | DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis. Uses autoencoder (AE) based latent-space features. Does not require any feature engineering and is easily applicable to high-dimensional search spaces. |
| Best-so-far trajectories | black-box | 1 | continuous | unconstrained | https://doi.org/10.1007/978-3-031-56852-7_7 | ? | Use raw optimisation (probing) trajectories containing the best performance values seen so far for each evaluation. |
| Current trajectories | black-box | 1 | continuous | unconstrained | https://doi.org/10.1007/978-3-031-56852-7_7 | ? | Use raw optimisation (probing) trajectories containing the current performance values seen at each evaluation. |
| Time series features using TSFRESH | black-box | 1 | continuous | unconstrained | https://doi.org/10.1145/3449639.3459399 | ? | Compute time series features using TSFRESH. The authors did this for internal CMA-ES variables, but this might also be applied to, e.g., performance trajectories. |
| Deep learning-based features | black-box | 1 | continuous | unconstrained | https://doi.org/10.1145/3512290.3528834 | ? | Represents the initially sampled points as point clouds to 2D images for use with deep-learning models. |
| Deep-ELA | black-box | 1, 2+ | continuous | unconstrained | https://doi.org/10.1162/evco_a_00372 | https://github.com/mvseiler/deep_ela | Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single-Objective and Multiobjective Continuous Optimization Problems. |
| Decomposition-Based Multi-objective Landscape Features | black-box | 2+ | continuous | unconstrained | https://doi.org/10.1007/978-3-030-72904-2_3 | ? | A set of landscape features for multi-objective combinatorial optimization, by decomposing the original multi-objective problem into a set of single-objective sub-problems. |
| SOO search tree | black-box | 1 | continuous | unconstrained | https://doi.org/10.1145/3299904.3340308 | ? | Features on the basis of the search tree constructed by the so-called SOO global optimizer. |
| Scalarization-based features | black-box | 2+ | continuous | unconstrained | https://doi.org/10.1145/3712256.3726378 | ? | Scalarization-based ELA (S-ELA) for multi-objective continuous optimization using objective scalarization methods: decomposition and non-dominated sorting. |
| Landscape Features for Multi-objective Interpolated Continuous Optimisation Problems | black-box | 2+ | continuous | unconstrained | https://doi.org/10.1145/3449639.3459353 | ? | Landscape Features for Multi-objective Interpolated Continuous Optimisation Problems aimed at measuring aspects of: global properties, multimodality, evolvability, and ruggedness. Uses a different sampling method to use features originally designed for discrete problems for the continuous case. |
| name | domain | objectives | variable type | constraints | reference | implementation | textual description |