Exploring Knowledge Transfer in Evolutionary Many-task Optimization: A Complex Network Perspective
- URL: http://arxiv.org/abs/2407.08918v1
- Date: Fri, 12 Jul 2024 01:49:04 GMT
- Title: Exploring Knowledge Transfer in Evolutionary Many-task Optimization: A Complex Network Perspective
- Authors: Yudong Yang, Kai Wu, Xiangyi Teng, Handing Wang, He Yu, Jing Liu,
- Abstract summary: We introduce a novel framework that employs a complex network to analyze the dynamics of knowledge transfer between tasks within EMaTO.
Our findings indicate that these networks are diverse, displaying community-structured directed graph characteristics, with their network density adapting to different task sets.
- Score: 8.968181160561894
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
Related papers
- Multi-Domain Evolutionary Optimization of Network Structures [25.658524436665637]
We develop a novel framework for multi-domain evolutionary optimization (MDEO)
Experiments on eight real-world networks of different domains demonstrate MDEO superiority in efficacy compared to classical evolutionary optimization.
Simulations of attacks on the community validate the effectiveness of the proposed MDEO in safeguarding community security.
arXiv Detail & Related papers (2024-06-21T04:53:39Z) - Towards Multi-Objective High-Dimensional Feature Selection via
Evolutionary Multitasking [63.91518180604101]
This paper develops a novel EMT framework for high-dimensional feature selection problems, namely MO-FSEMT.
A task-specific knowledge transfer mechanism is designed to leverage the advantage information of each task, enabling the discovery and effective transmission of high-quality solutions.
arXiv Detail & Related papers (2024-01-03T06:34:39Z) - Evaluating the structure of cognitive tasks with transfer learning [67.22168759751541]
This study investigates the transferability of deep learning representations between different EEG decoding tasks.
We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets.
arXiv Detail & Related papers (2023-07-28T14:51:09Z) - Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction [19.265502727154473]
This paper presents a visual analytics workflow for studying multivariate networks.
It consists of a neural-network-based learning phase to classify the data, a dimensionality reduction and optimization phase, and an interpreting phase conducted by the user.
A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret.
arXiv Detail & Related papers (2023-03-16T18:31:18Z) - Multiobjective Evolutionary Pruning of Deep Neural Networks with
Transfer Learning for improving their Performance and Robustness [15.29595828816055]
This work proposes MO-EvoPruneDeepTL, a multi-objective evolutionary pruning algorithm.
We use Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm.
Experiments show that our proposal achieves promising results in all the objectives, and direct relation are presented.
arXiv Detail & Related papers (2023-02-20T19:33:38Z) - MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion
Control in Real Networks [63.24965775030673]
We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms.
Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return.
We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
arXiv Detail & Related papers (2023-02-02T18:27:20Z) - Learning Good Features to Transfer Across Tasks and Domains [16.05821129333396]
We first show that such knowledge can be shared across tasks by learning a mapping between task-specific deep features in a given domain.
Then, we show that this mapping function, implemented by a neural network, is able to generalize to novel unseen domains.
arXiv Detail & Related papers (2023-01-26T18:49:39Z) - Transfer Learning Based Multi-Objective Evolutionary Algorithm for
Community Detection of Dynamic Complex Networks [1.693830041971135]
We propose a Feature Transfer Based Multi-Objective Optimization Algorithm (TMOGA) based on transfer learning and traditional multi-objective evolutionary algorithm framework.
We show that our algorithm can achieve better clustering effects compared with the state-of-the-art dynamic network community detection algorithms in diverse test problems.
arXiv Detail & Related papers (2021-09-30T17:16:51Z) - Learning Connectivity of Neural Networks from a Topological Perspective [80.35103711638548]
We propose a topological perspective to represent a network into a complete graph for analysis.
By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner.
This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks.
arXiv Detail & Related papers (2020-08-19T04:53:31Z) - Deep Multi-Task Learning for Cooperative NOMA: System Design and
Principles [52.79089414630366]
We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL)
We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
arXiv Detail & Related papers (2020-07-27T12:38:37Z) - Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding
Design for Multiuser MIMO Systems [59.804810122136345]
We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed.
An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed.
We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
arXiv Detail & Related papers (2020-06-15T02:57:57Z)
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.