Emergence of Novelty in Evolutionary Algorithms

Abstract

One of the main problems of evolutionary algorithms is the convergence of the population to local minima. In this paper, we explore techniques that can avoid this problem by encouraging a diverse behavior of the agents through a shared reward system. The rewards are randomly distributed in the environment, and the agents are only rewarded for collecting them first. This leads to an emergence of a novel behavior of the agents. We introduce our approach to the maze problem and compare it to the previously proposed solution, denoted as Novelty Search (Lehman and Stanley, 2011a). We find that our solution leads to an improved performance while being significantly simpler. Building on that, we generalize the problem and apply our approach to a more advanced set of tasks, Atari Games, where we observe a similar performance quality with much less computational power needed.

Residue-Based Natural Language Adversarial Attack Detection

“…no stopword changes) that limit the set of acceptable adversarial examples; and 2) distance constraints that only allow for a subset of the acceptable adversarial examples, where the distance constraint explicitly restricts the distance moved by an adversarial example from the original example to satisfy a specified imperceptibility threshold. This distance can be measured for example using the Universal Sentence Encoder (Herel et al, 2022). A sample subject to a specific NLP attack method (with defined pre-transformation constraints) will have an associated set of acceptable adversarial examples.…”