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% Optimal Foraging Theory Revisited: Abstract
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% (c) Copyright 2007 by Theodore P. Pavlic
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Optimal foraging theory explains adaptation via natural selection
through quantitative models. Behaviors that are most likely to be
favored by natural selection can be predicted by maximizing functions
representing Darwinian fitness. Optimization has natural applications in
engineering, and so this approach can also be used to design behaviors
of engineered agents. In this thesis, we generalize ideas from optimal
foraging theory to allow for its easy application to engineering design.
By extending standard models and suggesting new value functions of
interest, we enhance the analytical efficacy of optimal foraging theory
and suggest possible optimality reasons for previously unexplained
behaviors observed in nature. Finally, we develop a procedure for
maximizing a class of optimization functions relevant to our general
model. As designing strategies to maximize returns in a stochastic
environment is effectively an optimal portfolio problem, our methods are
influenced by results from modern and post-modern portfolio theory. We
suggest that optimal foraging theory could benefit by injecting updated
concepts from these economic areas.

%\textbf{Key words:} intelligent control; optimal foraging; choice;
%foraging theory; classical optimal foraging theory; OFT; optimal diet
%selection; predator; prey; patch; task-type choice; processing-length
%choice.