  Combining a spatiotemporal, multi-agent based model of a foraging ecosystem
with linear, genetically programmed rules for the agents' behaviors results in
implicit, endogenous, objective functions and selection algorithms based on
"natural selection". Use of this implicit optimization of genetic programs for
study of biological systems is tested by application to an artificial foraging
ecosystem, and compared with established biological, ecological, and stochastic
gene diffusion models. Limited program memory and execution time constraints
emulate real-time and concurrent properties of physical and biological systems,
and stress test the optimization algorithms. Relative fitness of the agents'
programs and efficiency of the resultant populations as functions of these
constraints gauge optimization effectiveness and efficiency. Novel solutions
confirm the creativity of the optimization process and provide an unique
opportunity to experimentally test the neutral code bloating hypotheses. Use of
this implicit, endogenous, evolutionary optimization of spatially interacting,
genetically programmed agents is thus shown to be novel, consistent with
biological systems, and effective and efficient in discovering fit and novel
solutions.
