Multifactorial Evolution: Toward Evolutionary Multitasking

Gupta, Abhishek; Ong, Yew-Soon; Feng, Liang

doi:10.1109/tevc.2015.2458037

Cited by 705 publications

(500 citation statements)

References 41 publications

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“…ATC can be extend to ATCS to deal with setup dependent scheduling problems). Clearly, transfer learning [7,33] and multi-task learning [8,40,108] will be very useful in automated design of production scheduling heuristics. The knowledge to solve a simple scheduling problem can be reused to solve hard problems and scheduling jobs at different machines can be based on some common pieces of knowledge.…”

Section: Machine Learningmentioning

confidence: 99%

Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

220

101

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Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

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Section: Machine Learningmentioning

confidence: 99%

Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

220

101

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“…Thereafter, a brief overview of the algorithm is provided. For a more complete discussion on the bio-cultural motivation behind the algorithm, its roots in memetic computation [16], the importance of describing an appropriate unified representation scheme, etc., the reader is referred to [8].…”

Section: The Multifactorial Evolutionary Algorithmmentioning

confidence: 99%

“…Recently in [8], a novel multifactorial evolutionary algorithm (MFEA) has been proposed as a means of exploiting the relationship between optimization tasks via the process of multitasking. The algorithm's nomenclature follows from the fact that each task is viewed as a unique factor influencing the evolution of a single population of individuals (artificial search agents).…”

Section: Introductionmentioning

confidence: 99%

Genetic transfer or population diversification? Deciphering the secret ingredients of evolutionary multitask optimization

Gupta

Ong

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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-Evolutionary multitasking has recently emerged as a novel paradigm that enables the similarities and/or latent complementarities (if present) between distinct optimization tasks to be exploited in an autonomous manner simply by solving them together with a unified solution representation scheme. An important matter underpinning future algorithmic advancements is to develop a better understanding of the driving force behind successful multitask problem-solving. In this regard, two (seemingly disparate) ideas have been put forward, namely, (a) implicit genetic transfer as the key ingredient facilitating the exchange of high-quality genetic material across tasks, and (b) population diversification resulting in effective global search of the unified search space encompassing all tasks. In this paper, we present some empirical results that provide a clearer picture of the relationship between the two aforementioned propositions. For the numerical experiments we make use of Sudoku puzzles as case studies, mainly because of their feature that outwardly unlike puzzle statements can often have nearly identical final solutions. The experiments reveal that while on many occasions "genetic transfer" and "population diversity" may be viewed as two sides of the same coin, the wider implication of genetic transfer, as shall be shown herein, captures the true essence of evolutionary multitasking to the fullest.

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“…The concept of multi-tasking optimization proposed in [30], [31] is able to simultaneously tackle multiple optimization tasks, which are defined as multifactorial optimization (MFO) problems. Meanwhile, multifactorial evolutionary algorithms (MFEAs) [30], [31] have been developed for addressing MFO problems.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, multifactorial evolutionary algorithms (MFEAs) [30], [31] have been developed for addressing MFO problems. MFEAs allow implicit knowledge transfers across different optimization tasks via two approaches, i.e., assortative mating and vertical cultural transmission.…”

Section: Introductionmentioning

confidence: 99%

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

Yang

Ding

Jin

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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Abstract-Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multi-tasking multi-objective evolutionary method to solve operational indices optimization, which involves a formulated multi-objective multifactorial operational indices optimization problem (MO-MFO) and a proposed multi-objective multifactorial optimization algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a dataset collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the multifactorial optimization algorithm, the assistant models are alternatively in multi-tasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multi-objective multifactorial optimization algorithm is applied to transfer knowledge among multi-tasking tasks. The proposed multi-tasking framework for operational indices optimization has conducted on 10 different production Conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem.Note to Practitioners−Operational indices optimization is a typical method to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multi-objective multi-tasking framework is developed to address the operational indices optimization, which includes a multi-taking multi-objective operational indices optimization problem formulation and a multi-taking multiobjective evolutionary optimization to solve the above formulated optimization problem. The proposed approach can achieve a solution set for the decision making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associate with a higher grade and yield of the product.

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Multifactorial Evolution: Toward Evolutionary Multitasking

Cited by 705 publications

References 41 publications

Genetic programming for production scheduling: a survey with a unified framework

Genetic programming for production scheduling: a survey with a unified framework

Genetic transfer or population diversification? Deciphering the secret ingredients of evolutionary multitask optimization

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

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