# Discovering Evolutionary Stepping Stones through Behavior Domination

## TL;DR

This paper introduces behavior domination, a framework for evolutionary algorithms that preserves diversity and useful stepping stones, outperforming existing methods in complex domains by combining theoretical guarantees with practical effectiveness.

## Contribution

The paper defines behavior domination as a new class of algorithms that maintain diversity and theoretical properties, introducing a novel fast non-dominated sorting algorithm for behavior-driven search.

## Key findings

- The new algorithm outperforms existing approaches in complex domains.
- Behavior domination preserves diversity better than previous methods.
- The approach scales effectively with problem complexity.

## 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 fitness measure to refocus search and help novelty search scale to more complex domains. However, combinations of novelty and fitness do not necessarily preserve the stepping stone discovery that novelty search affords. In several existing methods, competition between solutions can lead to an unintended loss of diversity. Behavior domination defines 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. The 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.