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Record W1600178424 · doi:10.1109/cec.2015.7257148

Evolving fractal art with a directed acyclic graph genetic programming representation

2015· article· en· W1600178424 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEvolutionary Algorithms and Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFractalFitness functionGenetic programmingRepresentation (politics)Computer scienceDigraphGraphGenetic representationMathematical optimizationTheoretical computer scienceMathematicsGenetic algorithmArtificial intelligenceDiscrete mathematics

Abstract

fetched live from OpenAlex

A class of fractals called orbit capture fractals are generated by iterating a function on a point until the point's trajectory enters a capture zone. This study uses a digraph based representation for genetic programming to evolve functions used to generate orbit capture fractals. Three variations on the genetic programming system are examined using two fitness functions. The first fitness function maximizes the entropy of the distribution of capture numbers, while the second places a geometric constraint on the distribution of capture numbers. Some combinations of representation and fitness function generate fractals often, while others yield interesting non-fractal images most of the time.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.731
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.264
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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