OFL/features.yaml at main · OpenOptimizationOrg/OFL · GitHub

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- name: Exploratory Landscape Analysis (ELA)
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained or box-constrained
  reference: https://doi.org/10.1145/2001576.2001690
  implementation: https://github.com/Reiyan/pflacco
  textual description: 'Exploratory Landscape Analysis (ELA) features'
- name: Cell Mapping Features
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained or box-constrained
  reference: https://doi.org/10.1007/978-3-319-07494-8_9
  implementation: https://github.com/Reiyan/pflacco
  textual description: 'Cell Mapping Techniques for Exploratory Landscape Analysis'
- name: Dispersion Features
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained or box-constrained
  reference: https://doi.org/10.1145/1143997.1144085
  implementation: https://github.com/Reiyan/pflacco
  textual description: 'The Dispersion Metric'
- name: Information Content-Based Features
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained or box-constrained
  reference: https://doi.org/10.1109/TEVC.2014.2302006
  implementation: https://github.com/Reiyan/pflacco
  textual description: 'Exploratory Landscape Analysis of Continuous Space Optimization Problems Using Information Content. Information Content of Fitness Sequences (ICoFiS).'
- name: Nearest Better Features
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained or box-constrained
  reference: https://doi.org/10.1145/2739480.2754642
  implementation: https://github.com/Reiyan/pflacco
  textual description: 'The Nearest-Better Features - also called Nearest-Better Clustering (NBC) Features. Features for detecting funnel structures.'
- name: Barrier Tree Features
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained or box-constrained
  reference: https://doi.org/10.1007/978-3-030-25147-5_7
  implementation: https://github.com/kerschke/flacco
  textual description: 'Following generalized cell mapping, barrier trees can be used to characterize the problem structure.'
- name: Principal Components Features
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained or box-constrained
  reference: https://doi.org/10.1007/978-3-030-25147-5_7
  implementation: https://github.com/Reiyan/pflacco
  textual description: 'Describe the variable scaling of continuous problems by applying a Principal Component Analysis.'
- name: Linear model features
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained or box-constrained
  reference: https://doi.org/10.1007/978-3-030-25147-5_7
  implementation: https://github.com/kerschke/flacco
  textual description: 'Following generalized cell mapping, barrier trees can be used to characterize the problem structure.'
- name: Multi-objective Combinatorial Problem Features
  domain: black-box
  objectives: '2+'
  variable type: binary/combinatorial
  constraints: unconstrained
  reference: https://doi.org/10.1162/evco_a_00193
  implementation: '?'
  textual description: 'Features describing Multi-objective Combinatorial Problems.'
- name: Local Optima Networks (LON)
  domain: black-box
  objectives: '1'
  variable type: combinatorial
  constraints: unconstrained
  reference: https://doi.org/10.1007/978-3-642-41888-4_9
  implementation: '?'
  textual description: 'Local Optima Networks (LON): A network-based model of combinatorial landscapes.'
- name: DynamoRep
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1162/evco_a_00370
  implementation: '?'
  textual description: '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.'
- name: Opt2Vec
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1016/j.ins.2024.121134
  implementation: '?'
  textual description: 'A continuous optimization problem representation based on algorithm behavior.'
- name: DoE2Vec
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1145/3583133.3590609
  implementation: https://github.com/nikivanstein/doe2vec
  textual description: '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.'
- name: Best-so-far trajectories
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1007/978-3-031-56852-7_7
  implementation: '?'
  textual description: 'Use raw optimisation (probing) trajectories containing the best performance values seen so far for each evaluation.'
- name: Current trajectories
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1007/978-3-031-56852-7_7
  implementation: '?'
  textual description: 'Use raw optimisation (probing) trajectories containing the current performance values seen at each evaluation.'
- name: Time series features using TSFRESH
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1145/3449639.3459399
  implementation: '?'
  textual description: '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.'
- name: Deep learning-based features
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1145/3512290.3528834
  implementation: '?'
  textual description: 'Represents the initially sampled points as point clouds to 2D images for use with deep-learning models.'
- name: Deep-ELA
  domain: black-box
  objectives: '1, 2+'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1162/evco_a_00372
  implementation: https://github.com/mvseiler/deep_ela
  textual description: 'Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single-Objective and Multiobjective Continuous Optimization Problems.'
- name: Decomposition-Based Multi-objective Landscape Features
  domain: black-box
  objectives: '2+'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1007/978-3-030-72904-2_3
  implementation: '?'
  textual description: '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.'
- name: SOO search tree
  domain: black-box
  objectives: '1'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1145/3299904.3340308
  implementation: '?'
  textual description: 'Features on the basis of the search tree constructed by the so-called SOO global optimizer.'
- name: Scalarization-based features
  domain: black-box
  objectives: '2+'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1145/3712256.3726378
  implementation: '?'
  textual description: 'Scalarization-based ELA (S-ELA) for multi-objective continuous optimization using objective scalarization methods: decomposition and non-dominated sorting.'
- name: Landscape Features for Multi-objective Interpolated Continuous Optimisation Problems
  domain: black-box
  objectives: '2+'
  variable type: continuous
  constraints: unconstrained
  reference: https://doi.org/10.1145/3449639.3459353
  implementation: '?'
  textual description: '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.'