Evolutionary Computation, Volume 16, Issue 4, Page 461-481, Winter 2008.
Texture Segmentation by Genetic Programming
Evolutionary Computation, Volume 16, Issue 4, Page 461-481, Winter 2008.
Category: Community Feeds
Automated Design of Image Operators that Detect Interest Points
Evolutionary Computation, Volume 16, Issue 4, Page 483-507, Winter 2008.
Evolutionary Computation, Volume 16, Issue 4, Page 483-507, Winter 2008.
Editorial Introduction to the Special Issue on Evolutionary Computer Vision
Evolutionary Computation, Volume 16, Issue 4, Page 437-438, Winter 2008.
Evolutionary Computation, Volume 16, Issue 4, Page 437-438, Winter 2008.
On the Choice of the Parent Population Size*
Evolutionary Computation, Volume 16, Issue 4, Page 557-578, Winter 2008.
Evolutionary Computation, Volume 16, Issue 4, Page 557-578, Winter 2008.
Human Body Pose Estimation with Particle Swarm Optimisation
Evolutionary Computation, Volume 16, Issue 4, Page 509-528, Winter 2008.
Evolutionary Computation, Volume 16, Issue 4, Page 509-528, Winter 2008.
An Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production
Evolutionary Computation, Volume 16, Issue 4, Page 529-555, Winter 2008.
Evolutionary Computation, Volume 16, Issue 4, Page 529-555, Winter 2008.
Genetic algorithms for battlefield operations
Marcelo De Brito of Genetic Argonaut pointed out an interesting article published by wired.com on top national security challenges for the next president.
One of these challenges is the use of genetic algorithms for battlefield operations. This reminded me of the project FOX-GA, which was one of the projects I’ve learned about while I was […]
Marcelo De Brito of Genetic Argonaut pointed out an interesting article published by wired.com on top national security challenges for the next president.
One of these challenges is the use of genetic algorithms for battlefield operations. This reminded me of the project FOX-GA, which was one of the projects I’ve learned about while I was at the Illinois Genetic Algorithms Laboratory.
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
Abstract:Data-intensive flow computing allows efficient processing of large volumes of data otherwise unapproachable. This paper introduces a new semantic-driven data-intensive flow infrastructure which: (1) provides a robust and transparent scalable solution from a laptop to large-scale clusters,(2) creates an unified solution for batch and interactive tasks in high-performance computing environments, and (3) encourages reusing and […]
Abstract:Data-intensive flow computing allows efficient processing of large volumes of data otherwise unapproachable. This paper introduces a new semantic-driven data-intensive flow infrastructure which: (1) provides a robust and transparent scalable solution from a laptop to large-scale clusters,(2) creates an unified solution for batch and interactive tasks in high-performance computing environments, and (3) encourages reusing and sharing components. Banking on virtualization and cloud computing techniques the Meandre infrastructure is able to create and dispose Meandre clusters on demand, being transparent to the final user. This paper also presents a prototype of such clustered infrastructure and some results obtained using it.
BILBOMD: Modeling flexible macromolecular systems using molecular dynamics and genetic algorithms
About a year ago I started a project with Michal Hammel from the Lawrence Berkeley National Lab on using a genetic algorithm for modeling flexible macromolecular systems (more specifically, the focus was on large proteins of over 900 amino acids).
To make the long story short, some proteins do not have rigid structure, but their […]
About a year ago I started a project with Michal Hammel from the Lawrence Berkeley National Lab on using a genetic algorithm for modeling flexible macromolecular systems (more specifically, the focus was on large proteins of over 900 amino acids).
To make the long story short, some proteins do not have rigid structure, but their structure changes over time. These proteins would typically contain several rigid modules, which are connected with flexible linkers. The goal of this project is to find out how the structure changes over time based on experimental results from solution-based SAXS (small angle X-ray scattering) and theoretical conformations computed with molecular dynamics (MD) simulations.
The basic idea of this approach called BILBOMD follows:
1. Compute experimental scattering profile using solution-based SAXS.
2. Compute a large number (1k to 10k) of potential conformations using molecular dynamics simulation.
3. Use a genetic algorithm to select a subset of conformations explaining the experimental scattering profile best.
The results are promising and we’re working on making these results even better and more useful.
A web page dedicated to this project can be found here.
OBUPM Presentations up
I want to thank everyone that participated in OBUPM-2008. The presentations were all very interesting and we had good participation from the audience. In particular I want to thank my fellow organizers, Martin Pelikan and Kumara Sastry. Without them, OBUPM would never have happened. Also thanks to Marc Ebner, the workshop chair, who had to […]
I want to thank everyone that participated in OBUPM-2008. The presentations were all very interesting and we had good participation from the audience. In particular I want to thank my fellow organizers, Martin Pelikan and Kumara Sastry. Without them, OBUPM would never have happened. Also thanks to Marc Ebner, the workshop chair, who had to deal with so many workshops at the same time.
Embedded below are four of the five presentations at OBUPM. When the last one is made available I will put it online also. These presentations can be downloaded from the OBUPM website here.
Thanks again and I hope to see you all at GECCO-2009!
Efficiency Enhancement of Estimation of Distribution Algorithms
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