Evolution and learning in artificial ecosystems

Paper in proceeding, 2018

A generic model is presented for ecosystems inhabited by artificial animals, or animats, that develop over time. The individual animats develop continuously by means of generic mechanisms for learning, forgetting, and decisionmaking.
At the same time, the animat populations develop in an evolutionary process based on fixed mechanisms for sexual and asexual reproduction, mutation, and death. The animats of the ecosystems move, eat, learn, make decisions, interact with other animats, reproduce, and die. Each animat has its individual sets of homeostatic variables, sensors, and motors.
It also has its own memory graph that forms the basis of its decision-making. This memory graph has an architecture (i.e. graph topology) that changes over time via mechanisms for adding and removing nodes. Our approach combines genetic algorithms, reinforcement learning, homeostatic decision-making, and dynamic concept formation. To illustrate the generality of the model, five examples of ecosystems are given, ranging from a simple
world inhabited by a single frog to a more complex world in which grass, sheep, and wolves interact.