The Struggle for Existence: Time, Memory and Bloat

TL;DR

This paper presents a multi-agent model of a foraging ecosystem that uses implicit, endogenous genetic programming and natural selection to study biological systems, emphasizing the effects of memory and bloat on optimization.

Contribution

It introduces a novel approach combining multi-agent modeling with implicit genetic programming to analyze biological and ecological systems, testing the neutral code bloat hypothesis.

Findings

Implicit genetic programming effectively models biological evolution.

Memory and execution constraints influence optimization outcomes.

The approach confirms the creativity and efficiency of endogenous evolution.

Abstract

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.