Titlebook: Data-Driven Evolutionary Optimization; Integrating Evolutio Yaochu Jin,Handing Wang,Chaoli Sun Book 2021 The Editor(s) (if applicable) and

Disaster

書目名稱Data-Driven Evolutionary Optimization影響因子(影響力)




書目名稱Data-Driven Evolutionary Optimization影響因子(影響力)學(xué)科排名




書目名稱Data-Driven Evolutionary Optimization網(wǎng)絡(luò)公開度




書目名稱Data-Driven Evolutionary Optimization網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data-Driven Evolutionary Optimization被引頻次




書目名稱Data-Driven Evolutionary Optimization被引頻次學(xué)科排名




書目名稱Data-Driven Evolutionary Optimization年度引用




書目名稱Data-Driven Evolutionary Optimization年度引用學(xué)科排名




書目名稱Data-Driven Evolutionary Optimization讀者反饋




書目名稱Data-Driven Evolutionary Optimization讀者反饋學(xué)科排名

五行打油詩

藐視

1860-949X Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included..978-3-030-74642-1978-3-030-74640-7Series ISSN 1860-949X Series E-ISSN 1860-9503

Peculate

Book 2021s deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included..

可轉(zhuǎn)變

Segmental Duration and Speech Timinge fitness predictions. Compared to the Gaussian process, dropout neural networks are scalable to the increase in the number of decision variables and the number of objectives, and are more suited to incremental learning, making it particularly attractive for solving high-dimensional many-objective e

流浪者

B. Geluvaraj,Meenatchi Sundaram strategy adopts a selective ensemble consisting of a subset of base learners chosen according to the search process. The third strategy builds a randomly sampled subsystem of the original system as the global model, and transfers its knowledge to a local surrogate. In addition, a method for selecti

流浪者

Evolutionary and Swarm Optimization,s that combine evolutionary search with local search, and estimation of distribution algorithms that use a probabilistic model to generate offspring solutions will also be described. Finally, basic methodologies for solving multi- and many-objective optimization problems are introduced.

膝蓋

Multi-surrogate-Assisted Single-objective Optimization,f the fitness landscape. The multiple surrogates can be used as an ensemble, in parallel, hierarchically, or in an interleaving way. Finally, we describe a method for adaptively selecting one surrogate at a particular search stage from a pool of surrogates according to their performance in the history.

Ankylo-

Protein

G. I. Marchouk,V. V. Shaydourovevaluating the quality of solutions and performance of optimization algorithms are described. A number of illustrative and real-world optimization problems are provided as examples in explaining the concepts and definitions.