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

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

Long-Term Evolution Experiment with Genetic Programming

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by tree depth. We suggest that deep expressions are resilient to learning as they disperse information, impeding evolvability, and the adaptation of highly nested organisms, and we argue instead for open complexity. Programs with more than 2,000,000,000 instructions (depth 20,000) are created by crossover. To support unbounded long-term evolution experiments in genetic programming (GP), we use incremental fitness evaluation and both SIMD parallel AVX 512-bit instructions and 16 threads to yield performance equivalent to 1.1 trillion GP operations per second, 1.1 tera GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server

Long-term evolution experiment with genetic programming [hot of the press]

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by their depth and suggest deep expressions are resilient to learning as they disperse information, impeding evolvability and the adaptation of highly nested organisms and instead we argue for open complexity. Programs with more than 2 000 000 000 instructions (depth 20 000) are created by crossover. To support unbounded long-term evolution experiments LTEE in GP we use incremental fitness evaluation and both SIMD parallel AVX 512 bit instructions and 16 threads to yield performance equivalent of up to 1.1 trillion GP operations per second, 1.1 tera-GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.Comment: 8 pages, 5 figures, preprint to appear in GECCO 2016, edits not yet made from reviewer comment

Media outlets and their moguls: why concentrated individual or family ownership is bad for editorial independence

This article investigates the levels of owner influence in 211 different print and broadcast outlets in 32 different European media markets. Drawing on the literature from industrial organisation, it sets out reasons why we should expect greater levels of influence where ownership of individual outlets is concentrated; where it is concentrated in the hands of individuals or families; and where ownership groups own multiple outlets in the same media market. Conversely, we should expect lower levels of influence where ownership is dispersed between transnational companies. The articles uses original data on the ownership structures of these outlets, and combines it with reliable expert judgments as to the level of owner influence in each of the outlets. These hypotheses are tested and confirmed in a multilevel regression model of owner influence. The findings are relevant for policy on ownership limits in the media, and for the debate over transnational versus local control of media

More on Gribov copies and propagators in Landau-gauge Yang-Mills theory

Fixing a gauge in the non-perturbative domain of Yang-Mills theory is a non-trivial problem due to the presence of Gribov copies. In particular, there are different gauges in the non-perturbative regime which all correspond to the same definition of a gauge in the perturbative domain. Gauge-dependent correlation functions may differ in these gauges. Two such gauges are the minimal and absolute Landau gauge, both corresponding to the perturbative Landau gauge. These, and their numerical implementation, are described and presented in detail. Other choices will also be discussed. This investigation is performed, using numerical lattice gauge theory calculations, by comparing the propagators of gluons and ghosts for the minimal Landau gauge and the absolute Landau gauge in SU(2) Yang-Mills theory. It is found that the propagators are different in the far infrared and even at energy scales of the order of half a GeV. In particular, also the finite-volume effects are modified. This is observed in two and three dimensions. Some remarks on the four-dimensional case are provided as well.Comment: 23 pages, 16 figures, 6 tables; various changes throughout most of the paper; extended discussion on different possibilities to define the Landau gauge and connection to existing scenarios; in v3: Minor changes, error in eq. (3) & (4) corrected, version to appear in PR