000			03903nam a22004215i 4500
999			_c398144 _d398144
001			398144
003			ES-MaUEC
005			20240430090131.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230329s2023 si | o |||| 0|eng d
020			_a9789811956508
024	7		_a10.1007/978-981-19-5650-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ334 _b2023 EB
100	1		_aFeng, Liang _eautor _4http://id.loc.gov/vocabulary/relators/aut _9681553
245	1	0	_aEvolutionary Multi-Task Optimization : _bFoundations and Methodologies _cby Liang Feng, Abhishek Gupta, Kay Chen Tan, Yew Soon Ong
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aMachine Learning: Foundations Methodologies and Applications _x2730-9916
505	0		_aChapter 1.Introduction -- Chapter 2. Overview and Application-driven Motivations of Evolutionary Multitasking -- Chapter 3.The Multi-factorial Evolutionary Algorithm -- Chapter 4. Multi-factorial Evolutionary Algorithm with Adaptive Knowledge Transfer -- Chapter 5.Explicit Evolutionary Multi-task Optimization Algorithm -- Chapter 6.Evolutionary Multi-task Optimization for Generalized Vehicle Routing Problem With Occasional Drivers -- Chapter 7. Explicit Evolutionary Multi-task Optimization for Capacitated Vehicle Routing Problem -- Chapter 8. Multi-Space Evolutionary Search for Large Scale Single-Objective Optimization -- Chapter 9.Multi-Space Evolutionary Search for Large-scale Multi-Objective Optimization.
520			_aA remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain's ability to generalize in optimization - particularly in population-based evolutionary algorithms - have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems, each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks. This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.
988			_aSpringer_Computer_2023
650		7	_2embne _9413115 _aInteligencia artificial
700	1		_9671485 _aGupta, Abhishek _eautor
700	1		_9690176 _aTan, K. C. _eautor
700	1		_9690177 _aOng, Yew Soon _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-5650-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ek _zSI