Evolutionarily Emergent Foraging Strategies for Active Agents

Abstract

Microbes, insects, birds, and mammals. Many forms of life depend on the search for food to survive. One search strategy that has been observed in nature is a levy flight, where an animal moves from area to area in long stretches to then explore the local environment. Levy flights can be described as statistical mathematical phenomena where the steps lengths of the agent’s movement follow a heavy tailed distribution. Earlier studies have shown that in certain environments, a middle ground between ballistic Levy flights and Brownian motion is more efficient than the outlier strategies. This thesis expands on those results by investigating which strategies perform best in an environment where local conditions change as one moves through space. We find that using a strategy that adapts to local conditions is not necessarily efficient if it does not consider the changing nature of the environment. We also let a neural network evolve using a genetic algorithm and let it optimize the movement of an agent which leads to efficient searches.