LEADER 03092nam 2200481z- 450 001 9911053037303321 005 20230911 035 $a(CKB)5690000000228525 035 $a(oapen)doab113956 035 $a(EXLCZ)995690000000228525 100 $a20230920c2023uuuu -u- - 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvances in Machine Learning and Mathematical Modeling for Optimization Problems 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2023 215 $a1 online resource (280 p.) 311 08$a3-0365-7741-6 330 $aMachine learning and deep learning have made tremendous progress over the last decade and have become the de facto standard across a wide range of image, video, text, and sound processing domains, from object recognition to image generation. Recently, deep learning and deep reinforcement learning have begun to develop end-to-end training to solve more complex operation research and combinatorial optimization problems, such as covering problems, vehicle routing problems, traveling salesman problems, scheduling problems, and other complex problems requiring general simulations. These methods also sometimes include classic search and optimization algorithms for machine learning, such as Monte Carlo Tree Search in AlphaGO. The present reprint contains all of the articles accepted and published in the Special Issue of Mathematics entitled "Advances in Machine Learning and Mathematical Modeling for Optimization Problems". The articles presented in this Special Issue provide insights into related fields, including models, performance evaluation and improvements, and application developments. We hope that readers will benefit from the insights of these papers and contribute to these rapidly growing areas. We also hope that this Special Issue will shed light on major developments in the area of machine learning and mathematical modeling for optimization problems and that it will attract the attention of the scientific community to pursue further investigations, leading to the rapid implementation of these techniques. 606 $aMathematics & science$2bicssc 606 $aResearch & information: general$2bicssc 610 $aartificial neural networks (ANNs) 610 $aconvex minimization problems 610 $adecision theory 610 $adeep reinforcement learning 610 $aend-to-end learning 610 $aevolutionary computation 610 $afeature selection 610 $amachine learning 610 $aoptimization problems 610 $apickup and delivery 610 $aresource allocation 610 $astatistical learning 610 $atraveling salesman problem 610 $avehicle routing problem 615 7$aMathematics & science 615 7$aResearch & information: general 906 $aBOOK 912 $a9911053037303321 996 $aAdvances in Machine Learning and Mathematical Modeling for Optimization Problems$94525094 997 $aUNINA