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

arXiv: https://arxiv.org/abs/2407.08918 · HTML: https://mlsanity.com/paper/2407.08918/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Tile Pattern KL-Divergence for Analysing and Evolving Game Levels](https://mlsanity.com/paper/1905.05077/) (2019)
- [Jack and Masters of all Trades: One-Pass Learning Sets of Model Sets From Large Pre-Trained Models](https://mlsanity.com/paper/2205.00671/) (2022)
- [Multi-Task Multi-Behavior MAP-Elites](https://mlsanity.com/paper/2305.01264/) (2023)
- [An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection](https://mlsanity.com/paper/2212.08854/) (2022)

## Abstract (arXiv)

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.