Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis

Liaw, Rung-Tzuo; Ting, Chuan-Kang

doi:10.1609/aaai.v33i01.33014295

Cited by 49 publications

(16 citation statements)

References 24 publications

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“…In MTMSO, each population is responsible for one unique task, and knowledge transfer across these population is implemented via the probabilistic crossover on these population's personal bests. In addition, there are many other EMTO solvers proposed in recent years [21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Self-Regulated Particle Swarm Multi-Task Optimization

Zheng

Zhou

et al. 2021

Sensors

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Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.

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Section: Introductionmentioning

confidence: 99%

Self-Regulated Particle Swarm Multi-Task Optimization

Zheng

Zhou

et al. 2021

Sensors

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“…Equipped with the capability of latent genetic transfer, Evolutionary Multitasking can resolve many optimization problems simultaneously by enabling the knowledge transfer among different problems through the unified chromosome representation. In control of the synergies of searching space for varying optimization tasks (Gupta et al, 2016a,b;Da et al, 2018;Zhou et al, 2018), Evolutionary Multitasking, which can be easily employed on existing population-based algorithm (Feng et al, 2017;Chen et al, 2018;Liu et al, 2018;Zhong et al, 2019), have shown promising results on a vast number of cases in multi-objective optimization (Gupta et al, 2016c;Feng et al, 2018), symbolic regression (Zhong et al, 2018a), capacitated vehicle routing problems (Zhou et al, 2016), expensive optimization tasks (Min et al, 2017), and can be extended to a large scale version (Chen et al, 2019;Liaw and Ting, 2019) to enable some more scalable applications in the future. The methodology of Evolutionary Multitasking paradigm naturally fits the multi-classification problem, by treating each binary classification problem as an optimization task within certain function evaluations.…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Zhong

2020

Front. Neurosci.

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Gene Expression Programming (GEP), a variant of Genetic Programming (GP), is a well established technique for automatic generation of computer programs. Due to the flexible representation, GEP has long been concerned as a classification algorithm for various applications. Whereas, GEP cannot be extended to multi-classification directly, and thus is only capable of treating an M-classification task as M separate binary classifications without considering the interrelationship among classes. Consequently, GEP-based multi-classifier may suffer from output conflict of various class labels, and the underlying conflict can probably lead to the degraded performance in multi-classification. This paper employs evolutionary multitasking optimization paradigm in an existing GEP-based multi-classification framework, so as to alleviate the output conflict of each separate binary GEP classifier. Therefore, several knowledge transfer strategies are implemented to enable the interation among the population of each separate binary task. Experimental results on 10 high-dimensional datasets indicate that knowledge transfer among separate binary classifiers can enhance multi-classification performance within the same computational budget.

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“…Studies focusing on many-tasking have been carried out in the domain singleobjective optimization [96,97]. Along similar lines, we extend our current experimental study to more than two multi-objective tasks.…”

Section: Study With More Than Two Tasksmentioning

confidence: 64%

“…Since the conceptualization of evolutionary multitasking, various algorithmic realizations have come to the fore [9,12,21,22,30,[51][52][53][54][55][56][57]. Notably, the crossover-based multifactorial evolutionary algorithms (i.e., MFEA and MO-MFEA) [21,22] have so far been the most prominent ones.…”

Section: The Multifactorial Evolutionary Algorithmsmentioning

confidence: 99%

“…In the literature, there have however been some recent works to e ciently handle many-task optimization [97,127]. While the direct scalability of MFEA-II/MO-MFEA-II to many-tasking can be achieved through simplifications such as reduced Over the recent years, the success of multitask optimization is not solely attributed to finding quality solutions to the problems, but also finding representations of the solutions for each problem that are more e cient for conveying knowledge transfer among tasks.…”

Section: Scalability To Many-taskingmentioning

confidence: 99%

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En route to automated extraction and transfer of knowledge in multitask optimization : an evolutionary perspective

Bali¹

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It is a conventional wisdom that real world problems seldom occur in isolation. The motivation for this work, inspired from the observation that humans rarely tackle every problem from scratch, is to improve optimization performance through adaptive knowledge transfer across related problems. The scope for spontaneous transfers under the simultaneous occurrence of multiple problems unveils the benefits of multitasking. Multitask optimization has recently demonstrated competence in solving multiple (related) optimization tasks concurrently. Notably, in the presence of underlying relationships between problems, the transfer of high quality solutions across them has shown to facilitate superior performance characteristics -as the cost of re-exploring overlapping regions of the search space is reduced. However, in the absence of any prior knowledge about the inter-task synergies (as is often the case with general black-box optimization), the threat of predominantly negative transfer prevails. Susceptibility to negative inter-task interactions can in fact be detrimental, often impeding the overall convergence behavior. To allay such fears, this thesis presents viable solutions towards automated extraction and transfer of (fruitful) knowledge such that any deleterious e↵ects of otherwise negative intertask exchanges are suppressed. To this end, an in-depth theoretical analysis is first conducted to unveil the primary caveats which concern the global convergence characteristics of the present day multitasking evolutionary optimization framework.Next, a novel evolutionary computation framework is proposed that enables online learning and exploitation of the similarities (and discrepancies) between distinct tasks in multitask settings via probabilistic mixture models. The proposed method is based on principled theoretical arguments that seek to minimize the tendency of harmful interactions between tasks, based on a purely data-driven learning of relationships among them. As a proof of concept, the method is initially validated experimentally on a wide range of synthetic discrete and continuous single-objective benchmarks. Thereafter, a realization of similar concepts of the proposed method is extended to the domain of multi-objective optimization (an omnipresent scenario in our daily lives). It is noteworthy that this work shall be among the first to utilize xiii xiv probabilistic modeling to capture inter-task relationships between multi-objective optimization tasks in the context of evolutionary multitasking. Empirical studies on a series of benchmark test functions show that the method is able to decipher and adapt to the degree of similarity between distinct multi-objective optimization tasks on the fly. Finally, the practicality of the proposed methods are substantiated on various real world case studies including reinforcement learning, multi-fidelity optimization and evolutionary deep learning. Not only do the practical studies provide insights into the behavior of the methods in the face of several (many) complex ...

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Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis

Cited by 49 publications

References 24 publications

Self-Regulated Particle Swarm Multi-Task Optimization

Self-Regulated Particle Swarm Multi-Task Optimization

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

En route to automated extraction and transfer of knowledge in multitask optimization : an evolutionary perspective

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