Evolutionary Inherited Neuromodulated Neurocontrollers with Objective Weighted Ranking
Ian Showalter, Howard M. Schwartz, Sidney Nascimento Givigi · 2021
In the physical world, individuals compete against others within their own population or separately evolving populations. Robotic agents will soon face this coevolutionary and adversarial reality. Many challenges not encountered in the evolution of single populations are encountered in adversarial coevolution. These challenges can render single evolutionary approaches either less effective or ineffective. Most problems have a fundamental goal, but also feature secondary desirable objectives. Here, evader agents in the pursuit-evasion game that do not elude capture are effectively useless. Secondly, when applied to evolve real robots online in a coevolutionary context, non-optimal robots can be erratic and cause damage. Objective hierarchy can be used to define the importance of each objective, and promote quicker optimization of primary objectives such as evasion. The Evolutionary Inherited Neuromodulated Neurocontroller (EINN) method incorporates objective weighted ranking (OWR), a novel objective hierarchy method that promotes optimization of the primary objective in simultaneous multi-objective optimization. EINN is compared to the previously demonstrated Lamarckian-inherited Neuromodulated MultiObjective Evolutionary Neurocontroller (LNMOEN), and shown to be effective in a single evolutionary context.