"This thesis presents a new class of evolutionary algorithms called mobile cellular evolutionary algorithms (mcEAs). These algorithms are characterized by individuals moving around on a spatial population structure. As a primary objective, this thesis aims to show that by controlling the population density and mobility in mcEAs, it is possible to achieve much better control over the rate of convergence than what is already possible in existing cellular EAs. Using the observations and results from this investigation into selection pressure in mcEAs, a general architecture for developing agent-based evolutionary algorithms called Artificial Ecosystems (AES) is presented. A simple agent-based EA is developed within the scope of AES is presented with two individual-based bottom-up schemes to achieve dynamic population sizing. Experiments with a test suite of optimization problems show that both mcEAs and the agent-based EA produced results comparable to the best solutions found by cellular EAs"--Abstract, page iii.
Agarwal, Sanjeev, 1971-
Madria, Sanjay Kumar
Tauritz, Daniel R.
M.S. in Computer Science
Missouri University of Science and Technology
ix, 61 pages
© 2010 Srinivasa Shivakar Vulli, All rights reserved.
Thesis - Open Access
Closed ecological systems -- Mathematical models
Computer algorithms -- Design
Evolutionary programming (Computer science)
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Electronic OCLC #
Link to Catalog Record
Vulli, Shivakar, "An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems" (2010). Masters Theses. 4800.