Grant: Discovery Grants Program - Individual
Brock University
$14,000.00
Multi-objective grammar-guided genetic programming for bio-network modelling and evolutionary design
FY 2015-16
Summary
This research uses artificial intelligence algorithms to automatically construct biological simulations, and solve problems in graphics and design. Genetic programming is an artificial intelligence technology that uses a simulation of Darwinian evolution to create computer programs. The research will improve some of the technical aspects of genetic programming, to improve its effectiveness at solving complex problems that are often very challenging for human beings to accomplish. For example, new ways of representing problem-solving programming languages will be derived. Improvements to evaluating potential solutions to problems will also be invented.
One application area of genetic programming that will be studied is the automatic construction of biological networks.For example, given some laboratory data denoting the changing quantities of proteins over time, the goal is to have genetic programming automatically use this data to construct a model that explains how this data arises in the lab. This is a challenging task because such laboratory data is usually very complex and noisy, and difficult to analyze. A goal of this research is to discover improved means for analyzing such data. In addition, new modeling languages will be used by genetic programming, to more effectively construct the models. Applications in evolutionary design will also be investigated. These are applications in which genetic programming can be used to create painterly art, 3D models and structures, and other applications in the fine arts. Such problems share many characteristics of biological modeling. For example, they often are complicated to evaluate, and they can benefit with improved programming language representations within the genetic programming system. The field of evolutionary design has been very popular with my students, and many have proceeded to graduate school to work in the area.
Program
Discovery Grants Program - Individual
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
138467-2011
Name nom
Ross, Brian
Department département
Computer Science
Organization id
22
Institution établissement
Brock University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2015
Competition year année de concours
2011
Award amount
14000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
405
Area of application group en
Environment
Area of application group fr
Environnement
Area of application en
Modelling and mathematical simulation of natural processes
Area of application fr
Modélisation et simulation mathématique des processus naturels
Research subject code
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Partie
5
Nb partie
5
Application title
Multi-objective grammar-guided genetic programming for bio-network modelling and evolutionary design
Keyword
NSERC
Application summary
This research uses artificial intelligence algorithms to automatically construct biological simulations, and solve problems in graphics and design. Genetic programming is an artificial intelligence technology that uses a simulation of Darwinian evolution to create computer programs. The research will improve some of the technical aspects of genetic programming, to improve its effectiveness at solving complex problems that are often very challenging for human beings to accomplish. For example, new ways of representing problem-solving programming languages will be derived. Improvements to evaluating potential solutions to problems will also be invented.
One application area of genetic programming that will be studied is the automatic construction of biological networks.For example, given some laboratory data denoting the changing quantities of proteins over time, the goal is to have genetic programming automatically use this data to construct a model that explains how this data arises in the lab. This is a challenging task because such laboratory data is usually very complex and noisy, and difficult to analyze. A goal of this research is to discover improved means for analyzing such data. In addition, new modeling languages will be used by genetic programming, to more effectively construct the models. Applications in evolutionary design will also be investigated. These are applications in which genetic programming can be used to create painterly art, 3D models and structures, and other applications in the fine arts. Such problems share many characteristics of biological modeling. For example, they often are complicated to evaluate, and they can benefit with improved programming language representations within the genetic programming system. The field of evolutionary design has been very popular with my students, and many have proceeded to graduate school to work in the area.