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City Berlin / Cambridge / New York City / East Lansing / / Company Princeton University Press / MIT Press / Ford Motor Company / / Country United States / United Kingdom / / / Event Natural Disaster / / Facility University of Washington / Massachusetts Institute of Technology Diversity / Carleton College / Michigan State University / / IndustryTerm ecology-based evolutionary algorithm / software engineering / partial-solutions / steady-state evolutionary algorithm / remote sensor network / satisfactory solutions / on-board control software / flight systems / software engineering approach / advocated software engineering practices / resilient solutions / Software engineering metric tasks / real-world software / evolutionary algorithm / energy efficiency / analysis tools / software-based systems / assurance systems / software engineering research / software engineering extension / digital evolution tool / evolutionary algorithms / software engineering tools / robotic systems / / NaturalFeature River Ribble / / Organization Michigan State University / Institute of Technology Diversity / Carleton College / National Science Foundation / United States Army / Princeton University / Massachusetts Institute of Technology / University of Washington / Seattle / / Person Dave Knoester / Blair / / / Position author / researcher / / ProvinceOrState New York / Massachusetts / Washington / Michigan / / Technology Introduction Evolutionary algorithms / evolutionary algorithm / 2012 Massachusetts Institute of Technology / steady-state evolutionary algorithm / 48824 sgoings@carleton.edu Abstract Evolutionary algorithms / ecology-based evolutionary algorithm / / SocialTag 
An ecology-based evolutionary algorithm to evolve solutions to complex problems Sherri Goings1, Heather Goldsby2, Betty H.C. Cheng3, and Charles Ofria3 1 Computer Science Dept., Carleton College, MN,