Incremental Evolution of Animats' Behaviors as a Multi-objective Optimization

Evolutionary algorithms have been successfully used to create controllers for many animats. However, intuitive fitness functions like the survival time of the animat, often do not lead to interesting results because of the bootstrap problem, arguably one of the main challenges in evolutionary robotics: if all the individuals perform equally poorly, the evolutionary process cannot start. To overcome this problem, many authors defined ordered sub-tasks to bootstrap the process, leading to an incremental evolution scheme. Published methods require a deep knowledge of the underlying structure of the analyzed task, which is often not available to the experimenter. In this paper, we propose a new incremental scheme based on multi-objective evolution. This process is able to automatically switch between each sub-task resolution and does not require to order them. The proposed method has been successfully tested on the evolution of a neuro-controller for a complex-light seeking simulated robot, involving 8 sub-tasks.