Optimising Genetic Algorithms for Use in Chemistry

Zoe Brain (SoCS CECS)

CS HDR MONITORING SISE Research Group

DATE: 2010-04-14
TIME: 14:00:00 - 14:30:00
LOCATION: RSISE Seminar Room, ground floor, building 115, cnr. North and Daley Roads, ANU
CONTACT: JavaScript must be enabled to display this email address.

ABSTRACT:
Research in the use of Meta-genetic algorithms to optimise the hunt for low-energy shapes of molecules.

Determining the electronic signature of long chain molecules is essential to the understanding of many biological processes, those involving molecular receptors in cells. Finding minimum energy conformers and thus electronic signatures of long-chain molecules by exhaustive search quickly becomes infeasible as the chain length increases. Typically, resources required are proportional to the number of possible conformers (shapes), which scales as O(3^/L/) where /L/ is the length. An optimised genetic algorithm that can determine the minimum energy conformer of an arbitrary long-chain molecule in a feasible time is described, using the tool, PyEvolve. The method is to first solve a generic problem for a long chain by exhaustive search, then by using the pre-determined results in a look-up table, to make use of a Meta-GA to optimize parameters of a simple GA through an evolutionary process to solve that same problem. By comparing the results using the tuned parameters obtained by this method with the results from exhaustive search on several molecules of comparable chain length we have obtained quantitative measurements of an increase in speed by a factor of three over standard parameter settings, and a factor of ten over exhaustive search.

BIO:
PhD student in Computer Science. http://cs.anu.edu.au/~Zoe.Brain