2016
DOI: 10.1007/s12559-016-9395-7
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Evolutionary Multitasking: A Computer Science View of Cognitive Multitasking

Abstract: The human mind possesses the most remarkable ability to perform multiple tasks with apparent simultaneity. In fact, with the present-day explosion in the variety and volume of incoming information streams that must be absorbed and appropriately processed, the opportunity, tendency, and (even) the need to multitask are unprecedented. Thus, it comes as little surprise that the pursuit of intelligent systems and algorithms that are capable of efficient multitasking is rapidly gaining importance among contemporary… Show more

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Cited by 221 publications
(110 citation statements)
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References 42 publications
(57 reference statements)
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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%
“…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%
“…Multi-task optimization (MTO) [7]- [10] applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking [9], or multi-factorial optimization (MFO) [8], is an emerging subfield of MTO, which integrates evolutionary computation and multi-task learning. It assumes that each constitutive task has some (positive) influence on D. Wu the evolutionary process of a single population of individuals, and hence evolving multiple populations from different tasks together simultaneously could be more efficient than evolving each individual task separately.…”
Section: Introductionmentioning
confidence: 99%
“…Based on the Darwinian theorem of "Survival of the Fittest" (Dawkins, 2006;Ma et al, 2014a), the population-based evolutionary algorithms (EAs) have been successfully used to solve a wide range of optimization problems (Deb, 2001;Qi et al, 2014;Ma et al, 2018). Multitasking optimization (MTO) problems have emerged as a new interest in the area of evolutionary computation Gupta et al, 2016a;Ong and Gupta, 2016;Yuan et al, 2016). Inspired by the ability of human beings to process multiple tasks at the same time, MTO aims at dealing with different optimization tasks simultaneously within a single solution framework.…”
Section: Introductionmentioning
confidence: 99%