Title

Evolutionary Algorithm Software Factory

Presenter Information

Brian Goldman

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

Comments

Joint project with Matthew Entrekin and Christopher Roush

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Apr 8th, 9:00 AM Apr 8th, 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.