A Preliminary Study of Knowledge Transfer in Multi-Classification Using Gene Expression Programming

Wei, Tingyang; Zhong, Jinghui

doi:10.3389/fnins.2019.01396

Cited by 12 publications

(4 citation statements)

References 53 publications

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“…In future work, we will study how to apply multiple population ideas for multitasking. Many researchers is interested in the approach ( Chen et al, 2020 ; Li, Zhang & Gao, 2018 ; Wei & Zhong, 2020 ; Wei et al, 2022 ; Xu et al, 2021 ). The approach’s advantages include: (1) each population evolves with different genetic operators, and each individual can be represented differently; (2) individuals migrate between populations.…”

Section: Conculsions and Future Workmentioning

confidence: 99%

Solving optimization problems simultaneously: the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm

Ban¹,

Pham²

2023

PeerJ Computer Science

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We studied two problems called the Traveling Repairman Problem (TRPTW) and Traveling Salesman Problem (TSPTW) with time windows. The TRPTW wants to minimize the sum of travel durations between a depot and customer locations, while the TSPTW aims to minimize the total time to visit all customers. In these two problems, the deliveries are made during a specific time window given by the customers. The difference between the TRPTW and TSPTW is that the TRPTW takes a customer-oriented view, whereas the TSPTW is server-oriented. Existing algorithms have been developed for solving two problems independently in the literature. However, the literature does not have an algorithm that simultaneously solves two problems. Multifactorial Evolutionary Algorithm (MFEA) is a variant of the Evolutionary Algorithm (EA), aiming to solve multiple factorial tasks simultaneously. The main advantage of the approach is to allow transferrable knowledge between tasks. Therefore, it can improve the solution quality for multitasks. This article presents an efficient algorithm that combines the MFEA framework and Randomized Variable Neighborhood Search (RVNS) to solve two problems simultaneously. The proposed algorithm has transferrable knowledge between tasks from the MFEA and the ability to exploit good solution space from RVNS. The proposed algorithm is compared directly to the state-of-the-art MFEA on numerous datasets. Experimental results show the proposed algorithm outperforms the state-of-the-art MFEA in many cases. In addition, it finds several new best-known solutions.

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Section: Conculsions and Future Workmentioning

confidence: 99%

Solving optimization problems simultaneously: the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm

Ban¹,

Pham²

2023

PeerJ Computer Science

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“…Island-EMT [167] Examination timetabling problem EMHH [78] Graph coloring problem EMHH [78] Minimum inter-cluster routing cost clustered tree problem (InterCluMRCT) CC-MFEA [65] Clustered shortest path tree problem (CluSTP) None [62], None [64], CC-MFEA [65], N-MFEA [68], N-MFEA [70] Real-world problem Machine learning Time series prediction problem MFGP [61] Performance prediction problem None [168] Gene regulatory network (GRN) reconstruction MMMA-FCM [169] Community detection MUMI [73] Chaotic time series prediction problem HD-MFEA neuroevolution [145] Training deep neural networks (DNN) problem AMTO [170], None [171] Fuzzy cognitive map (FCM) learning MMMA-FCM [169] Symbolic regression problem (SRP) MFGP [61] Multi-classification problem mXOF [138], EMC-GEP [172] Binary classification problem MFGP [59] Automatic hyperparameter tuning of machine learning models TEMO-MPS [109] Fuzzy system optimization problem MTGFS [72] Association mining problem MFEA [76] Classification problem DMSPSO [89], PSO-EMT [173], MMT-ELM [174] Table 3. Cont.…”

Section: Domain Problem Algorithmsmentioning

confidence: 99%

“…The MTO paradigm naturally fits the multi-classification problem by treating each binary classification problem as an optimization task within certain function evaluations. In the proposed framework, several knowledge transfer strategies (segment-based transfer, DE-based transfer, and feature transfer) were implemented to enable the interaction among the population of each separate binary task [172].…”

Section: Machine Learningmentioning

confidence: 99%

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Wang

et al. 2021

Mathematics

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Traditional evolution algorithms tend to start the search from scratch. However, real-world problems seldom exist in isolation and humans effectively manage and execute multiple tasks at the same time. Inspired by this concept, the paradigm of multi-task evolutionary computation (MTEC) has recently emerged as an effective means of facilitating implicit or explicit knowledge transfer across optimization tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems. An increasing number of works have thus been proposed since 2016. The authors collect the abundant specialized literature related to this novel optimization paradigm that was published in the past five years. The quantity of papers, the nationality of authors, and the important professional publications are analyzed by a statistical method. As a survey on state-of-the-art of research on this topic, this review article covers basic concepts, theoretical foundation, basic implementation approaches of MTEC, related extension issues of MTEC, and typical application fields in science and engineering. In particular, several approaches of chromosome encoding and decoding, intro-population reproduction, inter-population reproduction, and evaluation and selection are reviewed when developing an effective MTEC algorithm. A number of open challenges to date, along with promising directions that can be undertaken to help move it forward in the future, are also discussed according to the current state. The principal purpose is to provide a comprehensive review and examination of MTEC for researchers in this community, as well as promote more practitioners working in the related fields to be involved in this fascinating territory.

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“…Zheng et al [253] proposed an extension to the MFEA method in which a way of measuring the similarity of tasks is provided that allows knowledge transfer based on the degree of similarity. Wei et al [233] proposed an EMT-based classification method using Gene Expression Programming with several knowledge transfer strategies between tasks. Zhang et al [242,241] proposed a set of novel approaches for implicit knowledge sharing for solving DFJSS.…”

Section: Multi-task Learning For Evolutionary Algorithmsmentioning

confidence: 99%

Transfer Optimisation in Genetic Programming for Solving Uncertain Capacitated Arc Routing Problem

Ardeh¹

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Uncertain Capacitated Arc Routing Problem (UCARP) is a combinatorial optimisation problem with many important real-world applications. Genetic programming (GP) is a powerful machine learning technique that has been successfully used to automatically evolve routing policies for UCARP. Reusability is an open issue in the field of UCARP and in this direction, an open challenge is the case of scenario changes, e.g. change in the number of vehicles and probability distributions of random demands, which typically requires new training procedures to be initiated. Considering the expensive training cost of evolving routing policies for UCARP, a promising strategy is to learn and reuse knowledge from a previous problem-solving process to improve the effectiveness and efficiency of solving a new related problem, i.e. transfer learning. The overall goal of this thesis is to develop novel knowledge transfer algorithms for GP for solving UCARP to handle environment changes more effectively and efficiently. To fulfil this goal, a plethora of machine learning techniques, i.e. surrogate models, feature selection, searching and specialised genetic operators, are utilised in this thesis. First, this thesis explores the effectiveness of the existing transfer optimisation methods for solving UCARP. Accordingly, one of the main directions of this thesis is towards identifying the nature of transferable knowledge, which can impact the quality of knowledge transfer for GP to solve UCARP. For this purpose, a collection of the state-of-the-art transfer optimisation GP algorithms are evaluated for UCARP. After identifying some potential gaps in the literature, a number of preliminary transfer optimisation algorithms are proposed that supplement the literature. To evaluate the algorithms, a large set of knowledge transfer scenarios with various source and target problems were designed based on real-world datasets. According to the results, none of the methods showed significant improvement in the effectiveness of the trained UCARP routing policies. These results revealed the need for more effective transfer optimisation methods specifically designed for UCARP. Furthermore, our investigations revealed that the presence of duplicates in knowledge sources is one of the main challenges for effective transfer optimisation in solving UCARP. Second, we propose approaches to handling the presence of duplicates in the transferred knowledge. The first approach increases population diversity after knowledge transfer to counteract the loss of diversity that is introduced by the presence of duplicates in the transferred knowledge. In the second approach, the duplicates are removed from the transferred knowledge. Then, the transferred knowledge is utilised to create a diverse initial GP population of high-quality individuals. Both approaches are investigated through detailed experimental studies. The results indicate that, while the first approach did not perform better than GP with knowledge transfer, the second can improve the effectiveness of training routing policies with GP significantly. Third, this thesis proposes a novel algorithm that transfers the phenotypic characteristics of the routing policies for solving the source problem. In the new algorithm, the most fit and unique source routing policies are utilised for initialising GP for solving the target problem. Then, a tabu list is placed on the source routing policies and the GP process is prohibited from recreating any of the source routing policies. The motivation for this approach is that, due to the existence of similarity between the source and target problems, source routing policies are unlikely to have a good performance for the target problem. Our experimental studies confirmed that by prohibiting GP from recreating source policies, and the computational resources will be spent on searching and evaluating new regions of the search space, which can lead to discovering better solutions. Fourth, this thesis proposes a novel knowledge transfer algorithm based on the idea of maintaining the transferred knowledge as an auxiliary population. In this approach, first, the best individuals of the duplicate-free knowledge source are used to initialise GP. Additionally, these transferred individuals are also maintained as an auxiliary population and are evolved alongside the main population. To save the computational cost, the auxiliary population is evolved with a surrogate method. Additionally, an elaborate knowledge exchange mechanism between the two populations is devised that emphasises transferring high-quality and unique individuals, the transfer of which can improve the diversity of the receiving population. This allows GP to overcome the problem of losing its population diversity during the evolutionary process. Our detailed experimental results confirmed the superior performance of the proposed algorithm and confirmed that the proposed method improved the phenotypic diversity of GP population.

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A Preliminary Study of Knowledge Transfer in Multi-Classification Using Gene Expression Programming

Cited by 12 publications

References 53 publications

Solving optimization problems simultaneously: the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm

Solving optimization problems simultaneously: the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Transfer Optimisation in Genetic Programming for Solving Uncertain Capacitated Arc Routing Problem

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