Evolutionary Algorithm Software Factory
Department
Computer Science
Major
Computer Science
Research Advisor
Tauritz, Daniel R.
Advisor's Department
Computer Science
Funding Source
Missouri S&T Opportunities for Undergraduate Research Experiences (OURE) Program
Abstract
Evolutionary Algorithms (EAs) have shown great promise in solving complex real-world problems, such as typically found in manufacturing and transportation. There is a great lack of a standardized, thoroughly documented, and continuously maintained open-source research community resource containing industry quality implementations of the field’s classic and state-of-the-art algorithms. This project takes the first steps in providing this resource by creating a web-based software factory for EAs which takes as input an algorithm specification, and provides as output the associated source code in multiple programming languages. Pseudo-code for all the included EAs as well as links to the seminal papers in which those algorithms were published. This project will benefit researchers, educators, as well as practitioners that use EAs by providing a single resource to obtain standardized EA implementations.
Biography
Brian Goldman was born and raised in an unincorporated suburb of St. Louis, Missouri. He is currently a student of Computer Science with junior credit standing. His main focus in schooling is in the field of artificial intelligence and machine learning. In the future he plans to continue schooling, most likely in the hopes of attaining a PHD.
Research Category
Sciences
Presentation Type
Poster Presentation
Document Type
Poster
Location
Upper Atrium/Hallway
Presentation Date
08 Apr 2009, 9:00 am - 11:45 am
Evolutionary Algorithm Software Factory
Upper Atrium/Hallway
Evolutionary Algorithms (EAs) have shown great promise in solving complex real-world problems, such as typically found in manufacturing and transportation. There is a great lack of a standardized, thoroughly documented, and continuously maintained open-source research community resource containing industry quality implementations of the field’s classic and state-of-the-art algorithms. This project takes the first steps in providing this resource by creating a web-based software factory for EAs which takes as input an algorithm specification, and provides as output the associated source code in multiple programming languages. Pseudo-code for all the included EAs as well as links to the seminal papers in which those algorithms were published. This project will benefit researchers, educators, as well as practitioners that use EAs by providing a single resource to obtain standardized EA implementations.
Comments
Joint project with Matthew Entrekin and Christopher Roush