Title

Evolutionary Algorithm Software Factory

Presenter Information

Matthew Entrekin

Department

Computer Science

Major

Computer Science and Applied Mathematics

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

Matthew Entrekin is a junior studying Computer Science and Applied Mathematics. He grew up in Belleville, Illinois under his parents Steve and Jo Ann and his older sister Katie. In his spare time, he enjoys mentoring and teaching high school students at church, as well as playing racquetball and table tennis.

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 Brian Goldman 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.