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| Title: | An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems | |
| Author (s): | Vulli, Srinivasa Shivakar, 1982- | |
| Advisor(s): | Agarwal, Sanjeev Madria, Sanjay | |
| Department/Lab Affiliations: | Computer Science | |
| Issue Date: | 2010 | |
| Publisher: | Missouri University of Science and Technology | |
| Citation: | Vulli, Srinivasa S. "An artificial Life Approach to Evolutionary Computation: From Mobile Cellular Algorithms to Artificial Ecosystems." Master's Thesis, Computer Science, Missouri University of Science and Technology, 2010. | |
| Abstract: | "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, p. iii. | |
| Type: | Thesis/Dissertation text | |
| Copyright Notice: | These materials are protected under copyright by the original author. | |
| Link to this page: | ||
| URL: | ||
| Full Text: |
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| title | An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems | |
| contributor.advisor | Agarwal, Sanjeev | |
| contributor.advisor | Madria, Sanjay | |
| contributor.author | Vulli, Srinivasa Shivakar, 1982- | |
| contributor.deptlab | Airborne Reconnaissance and Image Analysis Laboratory. | |
| contributor.deptlab | Computer Science | |
| subject.LCSH | Closed ecological systems -- Mathematical models. | |
| subject.LCSH | Computer algorithms -- Design. | |
| subject.LCSH | Evolutionary computation. | |
| subject.LCSH | Evolutionary programming (Computer science) | |
| subject.LCSH | Multiagent systems. | |
| date.issued | 2010 | |
| publisher | Missouri University of Science and Technology | |
| identifier.URI | ||
| identifier.citation | Vulli, Srinivasa S. "An artificial Life Approach to Evolutionary Computation: From Mobile Cellular Algorithms to Artificial Ecosystems." Master's Thesis, Computer Science, Missouri University of Science and Technology, 2010. | |
| identifier.oclc | 648786130 | |
| description | Vita. | |
| description | The entire thesis text is included in file. | |
| description | Title from title screen of thesis/dissertation PDF file (viewed July 19, 2010) | |
| description | Thesis (M.S.)--Missouri University of Science and Technology, 2010. | |
| description | Includes bibliographical references (p. 57-60). | |
| description | System requirements: Adobe Acrobat Reader; Internet browser. | |
| description | Mode of access: World Wide Web. | |
| description.abstract | "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, p. iii. | |
| description. statementOfResponsibility | by Srinivasa Shivakar Vulli. | |
| type | Thesis/Dissertation | |
| type.DCMIType | text | |
| format.extent | ix, 61 p. : ill., digital, PDF file. | |
| language.ISO639-2 | eng | |
| rights | These materials are protected under copyright by the original author. | |
| date.accessioned | 2010-07-14T16:09:14Z | |
| identifier.persist.URI | ||
| date.available | 2010-07-19T19:59:56Z | |
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