A framework of grid-oriented genetic algorithms for large-scale optimization in bioinformatics

Hiroaki Imade; Ryohe Morishita; Isao Ono; Norihiko Ono; Masahiro Okamoto

doi:10.1109/CEC.2003.1299634

A framework of grid-oriented genetic algorithms for large-scale optimization in bioinformatics

Hiroaki Imade, Ryohe Morishita, Isao Ono, Norihiko Ono, Masahiro Okamoto

Systems Biology

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a framework for enabling for researchers of genetic algorithms (GAs) to easily develop GAs running on the grid, named «grid-oriented genetic algorithms (GOGAs)», and actually «gridify» a GA for estimating genetic networks, which is being developed by our group, in order to examine usability of the proposed GOGA framework. We also evaluate the scalability of the «gridified» GA by applying it to a five-gene genetic network estimation problem on a grid testbed constructed in our laboratory.

Original language	English
Pages	623-630
Number of pages	8
DOIs	https://doi.org/10.1109/CEC.2003.1299634
Publication status	Published - Jan 1 2003
Event	2003 Congress on Evolutionary Computation, CEC 2003 - Canberra, ACT, Australia Duration: Dec 8 2003 → Dec 12 2003

Other

Other	2003 Congress on Evolutionary Computation, CEC 2003
Country/Territory	Australia
City	Canberra, ACT
Period	12/8/03 → 12/12/03

All Science Journal Classification (ASJC) codes

Computational Mathematics

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10.1109/CEC.2003.1299634

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AU - Ono, Norihiko

AU - Okamoto, Masahiro

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AB - In this paper, we propose a framework for enabling for researchers of genetic algorithms (GAs) to easily develop GAs running on the grid, named «grid-oriented genetic algorithms (GOGAs)», and actually «gridify» a GA for estimating genetic networks, which is being developed by our group, in order to examine usability of the proposed GOGA framework. We also evaluate the scalability of the «gridified» GA by applying it to a five-gene genetic network estimation problem on a grid testbed constructed in our laboratory.

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A framework of grid-oriented genetic algorithms for large-scale optimization in bioinformatics

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