NEAT neural networks to control and simulate virtual creature's locomotion
Ahmed Tibermacine, Noureddine Djedi · 2014
This paper presents the application of evolutionary computation techniques for evolving behaviorisms in virtual creatures existing within a realistic virtual environment that are subject of the constraints as defined by the Newtonian model of physics. Evolutionary computation technique uses NEAT's network that is based on three fundamental principles (Genetic Encoding with Historical Markings, Speciation and Minimizing Dimensionality). The creatures' morphology is completely predetermined and is designed to elicit a variety of locomotive behaviors and test the generalization abilities of our framework. Three different morphologies are introduced into the simulation; each morphology represents an entirely different species of virtual creatures. The experiments show that NEAT's network is able to generate efficient locomotive behaviors.