Analysis of information gain and Kolmogorov complexity for structural evaluation of cellular automata configurations
- Submitting institution
-
University of Greenwich
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 24751
- Type
- D - Journal article
- DOI
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10.1080/09540091.2016.1151861
- Title of journal
- Connection Science
- Article number
- -
- First page
- 155
- Volume
- 28
- Issue
- 2
- ISSN
- 0954-0091
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Kolmogorov complexity (K), an algorithmic (descriptive) complexity, has been used to measure complexity, randomness, and information. However, it is independent of probability distribution, incomputable and one-dimensional. This paper proposes a novel approach to approximate K, measuring the complexity of 2D multi-state cellular automata (CA). The relevance of this approach, along with Information Gain, lies in providing an alternative to the dominant Shannon's entropy, which fails to discriminate structurally different patterns. Overcoming the shortcomings of entropic measures, researchers are now able to quantify the complexity of multi-state CA configurations, with potentially extendible implications for other 2D patterns.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
