Exploring Knowledge Transfer in Evolutionary Many-task Optimization: A Complex Network Perspective

TL;DR

This paper introduces a complex network-based framework to analyze and improve knowledge transfer mechanisms in evolutionary many-task optimization, highlighting the diversity and structure of transfer networks and their impact on algorithm performance.

Contribution

It presents a novel approach using complex network analysis to understand and enhance knowledge transfer in EMaTO, a previously underexplored area.

Findings

Knowledge transfer networks are diverse and community-structured.

Network density varies with different task sets.

Complex network analysis can improve EMaTO efficiency.

Abstract

The field of evolutionary many-task optimization (EMaTO) is increasingly recognized for its ability to streamline the resolution of optimization challenges with repetitive characteristics, thereby conserving computational resources. This paper tackles the challenge of crafting efficient knowledge transfer mechanisms within EMaTO, a task complicated by the computational demands of individual task evaluations. We introduce a novel framework that employs a complex network to comprehensively analyze the dynamics of knowledge transfer between tasks within EMaTO. By extracting and scrutinizing the knowledge transfer network from existing EMaTO algorithms, we evaluate the influence of network modifications on overall algorithmic efficacy. Our findings indicate that these networks are diverse, displaying community-structured directed graph characteristics, with their network density adapting to different task sets. This research underscores the viability of integrating complex network concepts into EMaTO to refine knowledge transfer processes, paving the way for future advancements in the domain.