Abstract
Biological chromosomes are replete with repetitive
sequences, microsatellites, 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 non-repeated 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.
- algorithms,
- artificial
- blocks,
- building
- computation,
- crossover,
- discipulus
- duplication
- elements,
- evolution,
- evolutionary
- finder,
- frequent
- genes,
- genetic
- genomes,
- gpengine,
- growth
- hierarchical
- microsatellites,
- of
- patterns,
- programming,
- repeated
- repeats
- repetitive
- sequences,
- ssr
- tandemly
- tracts,
- unequal
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