W B Langdon's 2004 Abstracts

W.B.Langdon . 4 Oct 2012 2004 papers , full list


Repeated Sequences in Linear GP Genomes

W.B. Langdon and W. Banzhaf. Late breaking paper at GECCO'2004 M.Keijzer, ed., Seattle, USA, 26-30 June 2004 PDF gzipped postscript).

ABSTRACT

Biological chromosomes are replete with repetitive sequences, microsatellites, SSR tracts, ALU, etc. in their DNA base sequences. We discover hierarchical repeating sequences (building blocks?) are evolved by genetic programming in linear Mackey Glass chaotic time series prediction programs.

Movie

Bibliographic details


Global Distributed Evolution of L-Systems Fractals

W. B. Langdon. Presented at EuroGP'2004, LNCS 3003 5-7 April 2004 Coimbra, Portugal, p308-315, (PDF at) Springer-Verlag. PDF ps.gz pfeiffer.html poster.

RN/04/13 gives all the snow flakes evolved during the trial period (PDF) (gzip postscript).

ABSTRACT

Internet based parallel genetic programming (GP) creates fractal patterns like Koch's snow flake. Pfeiffer, http://www.cs.ucl.ac.uk/staff/W.Langdon/pfeiffer.html by analogy with seed/embryo development, uses Lindenmayer grammars and LOGO style turtle graphics written in Javascript and Perl. 298 novel pictures were produced. Images are placed in animated snow globes (computerised snowstorms) by www web browsers anywhere on the planet. We discuss artificial life (Alife) evolving autonomous agents and virtual creatures in higher dimensions from a free format representation in the context of neutral networks, gene duplication and the evolution of higher order genetic operators.

Current active system pfeiffer.html

Bibliographic details


Genetic Programming for Mining DNA Chip data from Cancer Patients

W. B. Langdon and B. F. Buxton, Genetic Programming and Evolvable Machines, 5 (3): 251-257, September 2004 (doi:10.1023/B:GENP.0000030196.55525.f7, PDF, ps.gz)

ABSTRACT

In machine learning terms DNA (gene) chip data is unusual in having thousands of attributes (the gene expression values) but few (<100) records (the patients). A GP based method for both feature selection and generating simple models based on a few genes is demonstrated on cancer data.

Bibliographic details


Genetic Programming in Data Mining for Drug Discovery

W. B. Langdon and S. J. Barrett, Chapter 10 in Evolutionary Computing in Data Mining, Ashish Ghosh and Lakhmi C. Jain editors, Physica Verlag, pages 211-235, 2004. (PDF, ps.gz)

ABSTRACT

Genetic programming (GP) is used to extract from rat oral bioavailability (OB) measurements simple, interpretable and predictive QSAR models which both generalise to rats and to marketed drugs in humans. Receiver Operating Characteristics (ROC) curves for the binary classifier produced by machine learning show no statistical difference between rats (albeit without known clearance differences) and man. Thus evolutionary computing offers the prospect of in silico ADME screening e.g. for virtual chemicals, for pharmaceutical drug discovery.

Bibliographic details


up
W.B.Langdon cs.ucl.ac.uk