Evolutionary Multitask Optimization: Fundamental Research Questions, Practices, and Directions for the Future

• URL: http://arxiv.org/abs/2111.14463v3
• Date: Fri, 11 Nov 2022 09:40:50 GMT
• Title: Evolutionary Multitask Optimization: Fundamental Research Questions, Practices, and Directions for the Future
• Authors: Eneko Osaba, Javier Del Ser and Ponnuthurai N. Suganthan
• Abstract summary: This communication aims to engage the readership around a reflection on these issues, and to provide rationale why they remain unsolved. Specifically, we emphasize on three critical points of Evolutionary Multitasking Optimization. As a result of this research, we conclude that some important efforts should be directed by the community in order to keep the future of this promising field on the right track.
• Score: 10.330156481082698
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Transfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years. It is undeniable that the concepts underlying Transfer Optimization are formulated on solid grounds. However, evidences observed in recent contributions confirm that there are critical aspects that are not properly addressed to date. This short communication aims to engage the readership around a reflection on these issues, and to provide rationale why they remain unsolved. Specifically, we emphasize on three critical points of Evolutionary Multitasking Optimization: i) the plausibility and practical applicability of this paradigm; ii) the novelty of some proposed multitasking methods; and iii) the methodologies used for evaluating newly proposed multitasking algorithms. As a result of this research, we conclude that some important efforts should be directed by the community in order to keep the future of this promising field on the right track. Our ultimate purpose is to unveil gaps in the current literature, so that prospective works can attempt to fix these gaps, avoiding to stumble on the same stones and eventually achieve valuable advances in the area.

Related papers

• A Comprehensive Survey of Datasets, Theories, Variants, and Applications in Direct Preference Optimization [52.42860559005861]
  Direct Preference Optimization (DPO) has emerged as a promising approach for alignment. Despite DPO's various advancements and inherent limitations, an in-depth review of these aspects is currently lacking in the literature.
  arXiv  Detail & Related papers  (2024-10-21T02:27:24Z)
• Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought [61.588465852846646]
  Chain-of-Thought (CoT) reasoning has emerged as a promising approach for enhancing the performance of large language models (LLMs) In this work, we introduce a novel reasoning boundary framework (RBF) to address these challenges.
  arXiv  Detail & Related papers  (2024-10-08T05:26:28Z)
• Bridging the Gap Between Theory and Practice: Benchmarking Transfer Evolutionary Optimization [31.603211545949414]
  This paper pioneers a practical TrEO benchmark suite, integrating problems from the literature categorized based on the three essential aspects of Big Source Task-Instances: volume, variety, and velocity. Our primary objective is to provide a comprehensive analysis of existing TrEO algorithms and pave the way for the development of new approaches to tackle practical challenges.
  arXiv  Detail & Related papers  (2024-04-20T13:34:46Z)
• Two Complementary Perspectives to Continual Learning: Ask Not Only What to Optimize, But Also How [36.33674007527071]
  We argue that continual learning strategies should focus not only on the optimization objective, but also on the way this objective is optimized. We perform a series of pre-registered experiments combining replay-approximated joint objectives with gradient projection-based optimization routines. Our conceptual arguments, as well as some of our empirical results, demonstrate the distinctive importance of the optimization trajectory in continual learning.
  arXiv  Detail & Related papers  (2023-11-08T18:57:19Z)
• Parsing Objects at a Finer Granularity: A Survey [54.72819146263311]
  Fine-grained visual parsing is important in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms. We conduct an in-depth study of the advanced work from a new perspective of learning the part relationship.
  arXiv  Detail & Related papers  (2022-12-28T04:20:10Z)
• Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions [80.78077900288868]
  We decompose the design methodology of early-exit networks to its key components and survey the recent advances in each one of them. We position early-exiting against other efficient inference solutions and provide our insights on the current challenges and most promising future directions for research in the field.
  arXiv  Detail & Related papers  (2021-06-09T12:33:02Z)
• Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions [8.14509634354919]
  We consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The emerging paradigm of Evolutionary Multitasking tackles multitask optimization scenarios by using as inspiration concepts drawn from Evolutionary Computation.
  arXiv  Detail & Related papers  (2021-02-04T11:48:11Z)
• Unsupervised Transfer Learning for Spatiotemporal Predictive Networks [90.67309545798224]
  We study how to transfer knowledge from a zoo of unsupervisedly learned models towards another network. Our motivation is that models are expected to understand complex dynamics from different sources. Our approach yields significant improvements on three benchmarks fortemporal prediction, and benefits the target even from less relevant ones.
  arXiv  Detail & Related papers  (2020-09-24T15:40:55Z)
• Importance Weighted Policy Learning and Adaptation [89.46467771037054]
  We study a complementary approach which is conceptually simple, general, modular and built on top of recent improvements in off-policy learning. The framework is inspired by ideas from the probabilistic inference literature and combines robust off-policy learning with a behavior prior. Our approach achieves competitive adaptation performance on hold-out tasks compared to meta reinforcement learning baselines and can scale to complex sparse-reward scenarios.
  arXiv  Detail & Related papers  (2020-09-10T14:16:58Z)
• dMFEA-II: An Adaptive Multifactorial Evolutionary Algorithm for Permutation-based Discrete Optimization Problems [6.943742860591444]
  We propose the first adaptation of the recently introduced Multifactorial Evolutionary Algorithm II (MFEA-II) to permutation-based discrete environments. The performance of the proposed solver has been assessed over 5 different multitasking setups.
  arXiv  Detail & Related papers  (2020-04-14T14:42:47Z)
• COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking [9.54239662772307]
  We propose a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm.
  arXiv  Detail & Related papers  (2020-03-24T13:37:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.