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

Backstreaming from oil diffusion pumps Quarterly progress report, 1 Jan. - 31 Mar. 1966

Backstreaming from oil diffusion and turbo-molecular pump

More Mouldy Data: Another mycoplasma gene jumps the silicon barrier into the human genome

The human genome sequence database contains DNA sequences very like those of mycoplasma molds. It appears such moulds infect not only molecular Biology laboratories but were picked up by experimenters from contaminated samples and inserted into GenBank as if they were human. At least one mouldy EST (Expressed Sequence Tag) has transferred from public databases to commercial tools (Affymetrix HG-U133 plus 2.0 microarrays). We report a second example (DA466599) and suggest there is a need to clean up genomic databases but fear current tools will be inadequate to catch genes which have jumped the silicon barrier.Comment: data directory contains results of AF241217 and DA466599 blast runs by EBI in Cambridg

Repeated patterns in tree genetic programming

We extend our analysis of repetitive patterns found in genetic programming genomes to tree based GP. As in linear GP, repetitive patterns are present in large numbers. Size fair crossover limits bloat in automatic programming, preventing the evolution of recurring motifs. We examine these complex properties in detail: e.g. using depth v. size Catalan binary tree shape plots, subgraph and subtree matching, information entropy, syntactic and semantic fitness correlations and diffuse introns. We relate this emergent phenomenon to considerations about building blocks in GP and how GP works

Backstreaming from oil diffusion pumps Final report, Dec. 1, 1963 - May 30, 1966

Backstreaming from oil diffusion and turbomolecular pump

Convergence rates for the distribution of program outputs

Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain convergence theorems give general upper bounds on the linear program sizes needed for convergence. Tight bounds (exponential in N, N log N and smaller) are given for five computer models (any, average, cyclic, bit flip and Boolean). Mutation randomizes a genetic algorithm population in 1 4 (l + 1)(log(l) + 4) generations. Results for a genetic programming (GP) like model are confirmed by experiment.