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Simulating the evolution of recruitment behavior in foraging Ants


Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/12049

Simulating the evolution of recruitment behavior in foraging Ants

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Title: Simulating the evolution of recruitment behavior in foraging Ants
Author: Letendre, Kenneth
Advisor(s): Moses, Melanie
Committee Member(s): Forrest, Stephanie
Watson, Paul
Department: University of New Mexico. Dept. of Computer Science
Subject(s): Ants,Distributed-Problem Solving,Foraging,Genetic Algorithm,Optimization,Recruitment
LC Subject(s): Harvester ants--Food--Computer simulation.
Recruitment (Population biology)--Computer simulation.
Distributed cognition--Animal models.
Genetic algorithms.
Degree Level: Masters
Abstract: Spatial 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.
Graduation Date: December 2010
URI: http://hdl.handle.net/1928/12049

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