Comparison of Selection Methods in On-line Distributed Evolutionary Robotics

TL;DR

This paper evaluates how different selection methods affect the performance of on-line distributed evolutionary algorithms in multi-robot tasks, finding that higher selection pressure generally improves results, especially in complex scenarios.

Contribution

It introduces a variant of the mEDEA algorithm with a selection operator and compares four selection methods in multi-robot tasks, highlighting the impact of selection pressure.

Findings

Higher selection pressure improves task performance.

Small selection pressure suffices for simple tasks.

Performance gains are more significant in complex tasks.

Abstract

In this paper, we study the impact of selection methods in the context of on-line on-board distributed evolutionary algorithms. We propose a variant of the mEDEA algorithm in which we add a selection operator, and we apply it in a taskdriven scenario. We evaluate four selection methods that induce different intensity of selection pressure in a multi-robot navigation with obstacle avoidance task and a collective foraging task. Experiments show that a small intensity of selection pressure is sufficient to rapidly obtain good performances on the tasks at hand. We introduce different measures to compare the selection methods, and show that the higher the selection pressure, the better the performances obtained, especially for the more challenging food foraging task.