The Struggle for Existence in a Genetically Programmed Agent Based Model: Time, Memory and Bloat

A spatial-temporal agent based model with linear, genetically programmed agents competing and reproducing within the model results in implicit, endogenous objective functions and selection algorithms based on "natural selection". This implicit optimization of genetic programs is explored by application to an artificial foraging ecosystem. Limited computational resources of program memory and execution time emulate real-time and concurrent properties of physical and biological systems. Relative fitness of the agents' programs and efficiency of the resultant populations as functions of these computational resources are measured and compared. Surprising solutions for some configurations provide an unique opportunity to experimentally support neutral code bloating hypotheses. This implicit, endogenous, evolutionary optimization of genetically programmed agents is consistent with biological systems and is shown to be effective in both exploring the solution space and discovering fit, efficient, and novel solutions.