Discovering evolutionary stepping stones through behavior domination

Abstract

Behavior domination is proposed as a tool for understanding and harnessing the power of evolutionary systems to discover and exploit useful stepping stones. Novelty search has shown promise in overcoming deception by collecting diverse stepping stones, and several algorithms have been proposed that combine novelty with a more traditional tness measure to refocus search and help novelty search scale to more complex domains. However, combinations of novelty and tness do not necessarily preserve the stepping stone discovery that novelty search a ords. In several existing methods, competition between solutions can lead to an unintended loss of diversity. Behavior domination de nes a class of algorithms that avoid this problem, while inheriting theoretical guarantees from multiobjective optimization. Several existing algorithms are shown to be in this class, and a new algorithm is introduced based on fast non-dominated sorting. Experimental results show that this algorithm outperforms existing approaches in domains that contain useful stepping stones, and its advantage is sustained with scale. e conclusion is that behavior domination can help illuminate the complex dynamics of behavior-driven search, and can thus lead to the design of more scalable and robust algorithms.

Designing neural networks through neuroevolution

“…The space of quality diversity algorithms continues to expand [76][77][78][79][80] and is an exciting area of current research.…”

Creative AI Through Evolutionary Computation: Principles and Examples

“…By itself such parallel search would result in only a linear improvement, however, the main advantage is that the searches interact: if there is a good partial solution found in one of the searches, the others can immediately take advantage of it as well. That is, evolution finds building blocks, or schemata, or stepping stones, that are then combined to form better comprehensive solutions [6,11,21].…”

Enhanced optimization with composite objectives and novelty pulsation

“…Another version is to accept novel solutions only if they satisfy minimal performance criteria [17,32]. Some of these approaches have been generalized using the idea of behavior domination to discover stepping stones [34,35].…”