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dc.contributor.authorLetendre, Kenneth
dc.date.accessioned2011-02-08T20:34:12Z
dc.date.available2011-02-08T20:34:12Z
dc.date.issued2011-02-08
dc.date.submittedDecember 2010
dc.identifier.urihttp://hdl.handle.net/1928/12049
dc.description.abstractSpatial heterogeneity in the distribution of food is an important determinant of species' optimal foraging strategies, and of the dynamics of populations and communities. In order to explore the interaction of food heterogeneity and colony size in their effects on the behavior of foraging ant colonies, we built agent-based models of the foraging and recruitment behavior of harvester ants of the genus Pogonomyrmex. We optimized the behavior of these models using genetic algorithms over a variety of food distributions and colony sizes, and validated their behavior by comparison with data collected on harvester ants foraging for seeds in the field. We compared two models: one in which ants lay a pheromone trail each time they return to the nest with food; and another in which ants lay pheromone trails selectively, depending on the density of other food available in the area where food was found. We found that the density-dependent trail-laying model fit the field data better. We found that in this density-dependent recruitment model, colonies of all sizes evolved intense recruitment behavior, even when optimized for environments in which the majority of foods are distributed homogeneously. We discuss the implications of these models to the understanding of optimal foraging strategy and community dynamics among ants, and potential for application to ACO and other distributed problem-solving systems.en_US
dc.description.sponsorshipSandia National National Laboratories, Graduate Research Fellowship LDRD 09-1292en_US
dc.language.isoenen_US
dc.subjectAnts,Distributed-Problem Solving,Foraging,Genetic Algorithm,Optimization,Recruitmenten_US
dc.subject.lcshHarvester ants--Food--Computer simulation.
dc.subject.lcshRecruitment (Population biology)--Computer simulation.
dc.subject.lcshDistributed cognition--Animal models.
dc.subject.lcshGenetic algorithms.
dc.titleSimulating the evolution of recruitment behavior in foraging Antsen_US
dc.typeThesisen_US
dc.description.degreeComputer Scienceen_US
dc.description.levelMastersen_US
dc.description.departmentUniversity of New Mexico. Dept. of Computer Scienceen_US
dc.description.advisorMoses, Melanie
dc.description.committee-memberForrest, Stephanie
dc.description.committee-memberWatson, Paul


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