03379nam a22003974u 450000100220000000300060002200500170002800700150004500800410006002000300010102400310013104100080016210000230017024502220019325000060041526000160042126400160043730000120045333600260046533700260049133800360051734700210055334700080057452016500058265000370223265000380226965000330230765302380234070000200257870000180259870000210261677600360263785600640267385601020273785601420283910.1002/9781394178445Wiley20251030120311.0cr nn 008mam|a240722t2024||||xxu    fs|   |0|| 0|eng||  a9781394178445qelectronic7 a10.1002/97813941784452doi  aeng1 aZhang, Xingyi4aut10aEvolutionary Large‐Scale Multi‐Objective Optimization and ApplicationsbIntegrating Evolutionary Computation, Machine Learning, and Data Scienceh[electronic resource]cXingyi Zhang, Ran Cheng, Ye Tian, Yaochu Jin  a1  bWileyc2024 1bWileyc2024  a352pp.;  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext file2rdaft  bPDF  aTackle the most challenging problems in science and engineering with these cutting-edge algorithms Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach. Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it's a must-read for students and researchers facing these famously complex but crucial optimization problems. The book's readers will also find: Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more Discussion of benchmark problems and performance indicators for LSMOPs Presentation of a new taxonomy of algorithms in the field Evolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems. 4aSystems Engineering & Management 4aData Mining & Knowledge Discovery 4aIntelligent Systems & Agents00aMulti-objective optimization problems; evolutionary algorithms; large-scale multi-optimization problems; unconventional models; network science; machine learning; artificial intelligence; intensity modulated radiotherapy optimization1 aCheng, Ran4aut1 aTian, Ye4aut1 aJin, Yaochu4aut08iPrinted edition:z978139417841440uhttps://doi.org/10.1002/9781394178445aWiley Online Library40uhttps://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781394178445aWiley Online Library423Cover Imageuhttps://onlinelibrary.wiley.com/cms/asset/7c2afe61-5b69-4550-8559-06b0d20b0bb3/9781394178445.cover.gifaWiley Online Library