# ETO Meets Scheduling: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem

arXiv: https://arxiv.org/abs/2206.12902 · HTML: https://mlsanity.com/paper/2206.12902/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 26 Jun 2022.

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

- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense](https://mlsanity.com/paper/2004.09044/) (2020)
- [Geometric Understanding of Deep Learning](https://mlsanity.com/paper/1805.10451/) (2018)
- [Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control](https://mlsanity.com/paper/1810.01622/) (2018)

## Abstract (arXiv)

Evolutionary transfer optimization(ETO) serves as "a new frontier in evolutionary computation research", which will avoid zero reuse of experience and knowledge from solved problems in traditional evolutionary computation. In scheduling applications via ETO, a highly competitive "meeting" framework between them could be constituted towards both intelligent scheduling and green scheduling, especially for carbon neutrality within the context of China. To the best of our knowledge, our study on scheduling here, is the 1st work of ETO for complex optimization when multiobjective problem "meets" single-objective problems in combinatorial case (not multitasking optimization). More specifically, key knowledge like positional building blocks clustered, could be learned and transferred for permutation flow shop scheduling problem (PFSP). Empirical studies on well-studied benchmarks validate relatively firm effectiveness and great potential of our proposed ETO-PFSP framework.