Analysing the Regularity of Genomes Using Compression and Expression Simplification

Shin, Jungseok; Kang, Moonyoung; McKay, Bob; Nguyen, Xuan Phuong; Hoang, Tuan-Hao; Mori, Naoki; Essam, Daryl

doi:10.1007/978-3-540-71605-1_23

Cited by 8 publications

(6 citation statements)

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“…However, in real-world applications, problems generally include intron structures, which make the model and parameter inference much more difficult. For such problems, we consider that intron removal algorithms ( [13,30]) are effective, and application of such algorithms to GP-EDAs is left as a topic of future study.…”

Section: Discussionmentioning

confidence: 99%

Programming with Annotated Grammar Estimation

Hasegawa¹

2012

Genetic Programming - New Approaches and Successful Applications

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Section: Discussionmentioning

confidence: 99%

Programming with Annotated Grammar Estimation

Hasegawa¹

2012

Genetic Programming - New Approaches and Successful Applications

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“…Figure 3 Table VI shows the end-of-run values for these measurements 3 . Figures 2 and 3 are provided primarily for reference, since they are discussed in more detail in [13]. Points to note in the 3 Individual complexity is measured as the mean and standard deviation (over all runs) of the compression complexity of the best individual in the run; population complexity is measured as the mean (over all runs) of the compression complexity of the whole population.…”

Section: B Problem Domainmentioning

confidence: 99%

“…PPM uses a Markov model to condition the probability that a particular symbol will occur, on the sequence of characters which immediately precede the symbol. The length of context used to predict the next symbol is determined by the order of the Markov model (see [13] for details). XMLPPM extends this model to trees using a stack to record the context at each branch.…”

Section: ) String Vs Tree Compressionmentioning

confidence: 99%

“…Parallel to this development, we are studying the evolution of repeated patterns within GP individuals, in problems where repetition of components of solution structure is desirable [13]. We found tree compression a useful tool, providing an understanding of the extent of repetition within genotypes, and allowing comparison between the spread of code repetition under different GP algorithms, both in effective and in non-effective code.…”

Section: Introductionmentioning

confidence: 97%

“…In[13], we used both full and grow trees. With the former, XMLPPM can use a reduced character set for arithmetic coding of internal nodes, and hence get better compression: full trees are significantly non-random.…”

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confidence: 99%

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Using compression to understand the distribution of building blocks in genetic programming populations

McKay

Shin

Hoang

et al. 2007

2007 IEEE Congress on Evolutionary Computation

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Compression algorithms generate a predictive model of data, using the model to reduce the number of bits required to transmit the data (in effect, transmitting only the differences from the model). As a consequence, the degree of compression achieved provides an estimate of the level of regularity in the data. Previous work has investigated the use of these estimates to understand the replication of building blocks within Genetic Programming (GP) individuals, and hence to understand how different GP algorithms promote the evolution of repeated common structure within individuals. Here, we extend this work to the population level, and use it to understand the extent of similarity between sub-structures within individuals in GP populations.

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