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Using a Meta-GA for parametric optimization of simple Gas in the computational chemistry domain

Date

Authors

Addicoat, Matthew

Brain, Zoe

Publisher

Association for Computing Machinery Inc (ACM)

Abstract

The determination of the lowest energy conformer for long-chain molecules by exhaustive search methods quickly becomes infeasible as the length increases. Typically, resources required are proportional to the number of possible conformers (shapes), O(3n)

Keywords

Keywords: Agile programming; Carnosine; Conformera; Evolutionary computations; Genetic algorithm parameter optimization; Mete-Genetic algorithms; Parameter-tuning; PyEvolve; Calculations; Computational chemistry; Electric network parameters; Optimization; Parameter Agile programming; Carnosine; Conformera; Evolutionary computation; Genetic algorithm parameter optimization; Genetic algorithms; Mete-Genetic algorithms; Parameter tuning; Peptides; Potential energy surface; PyEvolve

URI

http://hdl.handle.net/1885/61230

Collections

ANU Research Publications

Source

GECCO’10 Proceedings of the 12th annual conference on Genetic and evolutionary computation

Type

Conference paper

DOI

10.1145/1830483.1830630

Restricted until

2037-12-31

Downloads

File

Description

01_Addicoat_Using_a_Meta-GA_for_parametric_2010.pdf (200.48 KB)

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