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62 changes: 62 additions & 0 deletions template.yaml
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# Please enter the information relevant to your problem/suite/generator.
#
# Instructions:
# - Fields that are not relevant can be removed (delete the whole line), or left empty.
# - Enter 'unknown' for fields where the value is unknown.
# - If multiple suggested values are relevant, include all those that fit.
# - Suggested values for fields are recommendations. If they are not a good fit, feel free to add something else (and ideally, explain why).
# - If some property is present, it does not have to apply to all problems/variables.
- name:
short: BBOB # Short name or abbreviation
full: Real-Parameter Black-Box Optimization Benchmarking # Full name
suite/generator/single: suite # Whether this is a single problem, a problem suite, or problem generator
objectives:
number: '1' # (list of) fixed numbers, or ranges (5-11,19,85), scalable if it can be any value
variables: # Information about the input variables
types: continuous # Can be one or more of (continuous, integer, binary, categorical, mixed) in case of mixed, describe which mixes are possible
conditional: 'no' # Whether there are conditional dependencies between variables, 'yes' or 'no'
dimensionality:
scalable: 'yes' # Is the dimensionality scalable yes or no
number: '2-40' # (list of) fixed numbers, or ranges (5-11,19,85), or 'any' if it can be any value
constraints:
present: 'no' # Whether there are constraints yes or no
soft: 'no' # Whether there are soft constraints yes or no
hard: 'no' # Whether there are hard constraints yes or no
number: '0' # How many constraints there are in total, fixed numbers or range, scalable if it can by any value
boundary/box: 'yes' # Is the problem box-constrained yes, no, or some (if only some variables are bounded)
permutation: 'no' # Variables must be a permutation yes, no, or some
dynamic: # Dynamic properties, e.g., changing objective functions
present: 'no' # Enter, yes, no, or optional
types: '' # Types of dynamic behaviour that are present
noise: # Noise on the objective function(s)
present: 'no' # Enter, yes, no, or optional
types: '' # Types of noise that are present
modality:
types: 'unimodal, multimodal' # Enter all that are present
evaluations:
multi-fidelity: 'no' # Whether evaluations can be performed at multiple fidelities, yes, no, or optional
partial possible: 'no' # Whether evaluation of subset(s) of decision variables are possible
independent objectives: 'no' # Can objective functions be evaluated independently of each other yes or no
reference:
links:
- https://doi.org/10.1080/10556788.2020.1808977 # URL to source or closely related research article, preferably DOI based. Add a new line below for each additional article.
authors: 'Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar, Dimo Brockhoff'
contact person: ''
implementations:
- name: COCO # Name of the tool/library/package
link: https://github.com/numbbo/coco
languages: 'C/C++, Python, Java, Matlab/Octave' # Programming languages the implementation can be used with
evaluation time: 'less than a second' # Choose: 'less than a second', 'seconds', 'minutes', 'hours', 'days'
specific requirements: 'no' # Can be things like memory/GPU requirements, or the need for a specific simulator. Enter 'no' if none.
source:
origin: 'artificial' # Is the problem artificial, real-world, or real-world-like
real-world: # In case it is real-world (like), provide details
domain: '' # E.g., automotive
open/closed: '' # Is the problem open source yes or no
textual description:
general info: ''
motivation: 'evaluate algorithm performance for typical difficulties that occur in continuous problems'
challenge/key characteristics: ''
limitations: ''
example links: '' # URLs to examples where it was used (e.g., scientific articles) - Note: URLs to where it was proposed should go under the 'reference' field.
other info: ''