Immuno-inspired management of halls of fame for embodied evolution.

Evolutionary robotics employs unconventional techniques to continuously evolve controllers for robots, based on their fitness values. In most cases, a parent controller is subjected to mutation to evolve its offspring. If the offspring performs better than the parent, the former is made to replace the latter. This essentially results in a major loss in information learned by the parents over generations. One simple workaround to circumvent this problem is to maintain a Hall of Fame (HoF) comprising the best parent controllers for use in future generations. In embodied evolutionary robotic scenarios, caching a large number of controllers in an HoF would result in increased computational overheads while selecting the best out of them, resulting in a drastic reduction in performance. With no means to find an upper limit to the number of controllers that can populate an HoF a priori , devising a technique to dynamically regulate this population is imperative. In this work, a novel method to evict the non-performing controllers within an HoF based on the dynamics of the system, is proposed. We describe the evolution of such controllers using genetic operators that eventually form an Idiotypic Network. A constantly varying Resource , associated with each controller together with its concentration in the Idiotypic Network, helps decide its eviction from the HoF. Experiments performed using simulations and also a real robot indicate a marked improvement in the learning process due to the dynamic eviction policy. [ABSTRACT FROM AUTHOR]