1,867 research outputs found
Repeated sequences in linear genetic programming genomes
Biological chromosomes are replete with repetitive sequences, micro
satellites, SSR tracts, ALU, etc. in their DNA base sequences. We
started looking for similar phenomena in evolutionary computation.
First studies find copious repeated sequences, which can be hierarchically
decomposed into shorter sequences, in programs evolved using
both homologous and two point crossover but not with headless chicken
crossover or other mutations. In bloated programs the small number
of effective or expressed instructions appear in both repeated and nonrepeated
code. Hinting that building-blocks or code reuse may evolve
in unplanned ways.
Mackey-Glass chaotic time series prediction and eukaryotic protein
localisation (both previously used as artificial intelligence machine
learning benchmarks) demonstrate evolution of Shannon information
(entropy) and lead to models capable of lossy Kolmogorov compression.
Our findings with diverse benchmarks and GP systems suggest
this emergent phenomenon may be widespread in genetic systems
A generalization of periodic autoregressive models for seasonal time series
Many nonstationary time series exhibit changes in the trend and seasonality structure, that may be modeled by splitting the time axis into different regimes. We propose multi-regime models where, inside each regime, the trend is linear and seasonality is explained by a Periodic Autoregressive model. In addition, for achieving parsimony, we allow season grouping, i.e. seasons may consists of one, two, or more consecutive observations. Since the set of possible solutions is very large, the choice of number of regimes, change times and order and structure of the Autoregressive models is obtained by means of a Genetic Algorithm, and the evaluation of each possible solution is left to an identication criterion such as AIC, BIC or MDL. The comparison and performance of the proposed method are illustrated by a real data analysis. The results suggest that the proposed procedure is useful for analyzing complex phenomena with structural breaks, changes in trend and evolving seasonality
An evolutionary model with Turing machines
The development of a large non-coding fraction in eukaryotic DNA and the
phenomenon of the code-bloat in the field of evolutionary computations show a
striking similarity. This seems to suggest that (in the presence of mechanisms
of code growth) the evolution of a complex code can't be attained without
maintaining a large inactive fraction. To test this hypothesis we performed
computer simulations of an evolutionary toy model for Turing machines, studying
the relations among fitness and coding/non-coding ratio while varying mutation
and code growth rates. The results suggest that, in our model, having a large
reservoir of non-coding states constitutes a great (long term) evolutionary
advantage.Comment: 16 pages, 7 figure
Integer Set Compression and Statistical Modeling
Compression of integer sets and sequences has been extensively studied for
settings where elements follow a uniform probability distribution. In addition,
methods exist that exploit clustering of elements in order to achieve higher
compression performance. In this work, we address the case where enumeration of
elements may be arbitrary or random, but where statistics is kept in order to
estimate probabilities of elements. We present a recursive subset-size encoding
method that is able to benefit from statistics, explore the effects of
permuting the enumeration order based on element probabilities, and discuss
general properties and possibilities for this class of compression problem
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