NeuroEvolution of Augmenting Toplogies (NEAT) is a genetic algorithm that can evolve networks of unbound complexity by starting from simple networks and "complexifying" through different genetic operators. It has been used to train agents to play Super Mario World and generate "genetic art".
Multi-Objective Optimization is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.
I propose a project where I implement a NEAT framework and use it to optimize single-objective functions within ensmallen. Besides this, I will implement a framework for multi-objective optimization within ensmallen along with a multi-objective optimizer, Unified NSGA-III. If time allows, I will use these two frameworks to implement MultiModal NEAT (MM-NEAT) to train an agent in multi-objective reinforcement learning environments.