A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking

Liang, Zhengping; Zhang, Jian; Feng, Liang; Zhu, Zexuan

doi:10.1016/j.eswa.2019.07.015

Cited by 76 publications

(33 citation statements)

References 37 publications

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“…In general, conventional optimization problems can be divided into two categories: single-objective optimization (SOO) problems and multiobjective optimization (MOO) problems (Liang et al, 2019). They are both committed to seeking the optimal solution of an optimization task.…”

Section: Multitask Optimizationmentioning

confidence: 99%

“…Currently, the research on EMT can approximately be summarized into three categories, the practical application of EMT (Sagarna and Ong, 2016;Yuan et al, 2016;Zhou et al, 2016;Cheng et al, 2017;Binh et al, 2018;Thanh et al, 2018;Lian et al, 2019;Wang et al, 2019) and the improved algorithm based on the MFEA framework (Bali et al, 2017;Feng et al, 2017;Wen and Ting, 2017;Joy et al, 2018;Tuan et al, 2018;Zhong et al, 2018;Binh et al, 2019;Liang et al, 2019;Yin et al, 2019;Yu et al, 2019;Zheng et al, 2019;Zhou et al, 2019) and the perfection of EMT theory (Gupta et al, 2016a;Hashimoto et al, 2018;Liu et al, 2018;Zhou et al, 2018;Bali et al, 2019;Chen et al, 2019;Feng et al, 2019;Huang et al, 2019;Shang et al, 2019;Song et al, 2019;Tang et al, 2019). From the above studies, a consensus can be summarized that efficiently utilizing the inter-task related information is the key to improve overall search efficiency in EMT.…”

Section: Introductionmentioning

confidence: 99%

“…The improved evolutionary mechanism takes both the implicit transfer of useful features between tasks and the ability of exploration into account. Liang et al (2019) introduced genetic transform strategy and hyper-rectangle search strategy to the MFEA to improve the efficiency of knowledge transfer between tasks in the late iteration of the traditional MFEA. Huang et al (2019) proposed an efficient surrogate-assisted multitask evolutionary framework with adaptive knowledge transfer, which is very superior for solving expensive optimization tasks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

Zhang

et al. 2020

Front. Neurorobot.

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In recent years, lots of multifactorial optimization evolutionary algorithms have been developed to optimize multiple tasks simultaneously, which improves the overall efficiency using implicit genetic complementarity between different tasks. In this paper, a novel multitask fireworks algorithm is proposed with novel transfer sparks to solve multitask optimization problems. For each task, some transfer sparks would be generated with adaptive length and promising direction vector, which are very helpful to transfer useful genetic information between different tasks. Finally, the proposed algorithm is compared against some chosen state-of-the-art evolutionary multitasking algorithms. The experimental results show that the proposed algorithm provides better performance on several single objectives and multiobjective MTO test suites.

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Section: Multitask Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

Zhang

et al. 2020

Front. Neurorobot.

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“…Researchers proposed a variety of techniques, such as multi-factorial memetic algorithm (Chen et al, 2017a), opposition-based learning , crosstask search direction , explicit autoencoding (Feng et al, 2018), and cooperative co-evolutionary memetic algorithm (Chen et al, 2017b), for the purpose of solving the multi-tasking problem. Evolutionary multi-tasking algorithms share knowledge among individual tasks and accelerate the convergence of multiple optimization tasks (Liang et al, 2019).…”

Section: Multi-tasking Intelligencementioning

confidence: 99%

Droplet-Transmitted Infection Risk Ranking Based on Close Proximity Interaction

Guo

Shi

et al. 2020

Front. Neurorobot.

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We propose an automatic method to identify people who are potentially-infected by droplet-transmitted diseases. This high-risk group of infection was previously identified by conducting large-scale visits/interviews, or manually screening among tons of recorded surveillance videos. Both are time-intensive and most likely to delay the control of communicable diseases like influenza. In this paper, we address this challenge by solving a multi-tasking problem from the captured surveillance videos. This multi-tasking framework aims to model the principle of Close Proximity Interaction and thus infer the infection risk of individuals. The complete workflow includes three essential sub-tasks: (1) person re-identification (REID), to identify the diagnosed patient and infected individuals across different cameras, (2) depth estimation, to provide a spatial knowledge of the captured environment, (3) pose estimation, to evaluate the distance between the diagnosed and potentially-infected subjects. Our method significantly reduces the time and labor costs. We demonstrate the advantages of high accuracy and efficiency of our method. Our method is expected to be effective in accelerating the process of identifying the potentially infected group and ultimately contribute to the well-being of public health.

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“…Therefore, the transfer of useful genetic solutions can be conducted simply by multiplication operation with the learned M. It is very recently that a novel genetic transform strategy has been proposed [37]. Given two tasks T 1 and T 2 , two mapping vectors M 12 (from T 1 to T 2 ) and M 21 (from T 2 to T 1 ) are calculated as follows:…”

Section: Multi-population Evolution Modelmentioning

confidence: 99%

Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

Wang¹,

Xu²,

Rong³

et al. 2019

IJCIS

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A B S T R A C TMulti-task optimization algorithm is an emergent paradigm which solves multiple self-contained tasks simultaneously. It is thought that multi-factorial evolutionary algorithm (MFEA) can be seen as a novel multi-population algorithm, wherein each population is represented independently and evolved for the selected task only. However, the theoretical and experimental evidence to this conclusion is not very convincing and especially, the coincidence relation between MFEA and multi-population evolution model is ambiguous and inaccurate. This paper aims to make an in-depth analysis of this relationship, and to provide more theoretical and experimental evidence to support the idea. In this paper, we clarify several key issues unsettled to date, and design a novel across-population crossover approach to avoid population drift. Then MFEA and its variation are reviewed carefully in view of multi-population evolution model, and the coincidence relation between them are concluded. MFEA is completely recoded along with this idea and tested on 25 multi-task optimization problems. Experimental results illustrate its rationality and superiority. Furthermore, we analyze the contribution of each population to algorithm performance, which can help us design more efficient multi-population algorithm for multi-task optimization. MTO paradigm [6]. If the optimization tasks happen to bear some commonality or complementarity, then the inclusion of knowledge transfer often leads to significant performance improvements relative to conventional EAs alone.

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A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking

Cited by 76 publications

References 37 publications

A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

Droplet-Transmitted Infection Risk Ranking Based on Close Proximity Interaction

Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

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