Optimization and Machine Learning. Patrick Siarry

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      Introduction

       Rachid CHELOUAH

       CY Cergy Paris University, France

      Machine learning is revolutionizing our world. It is difficult to conceive of any other information technology that has developed so rapidly in recent years, in terms of real impact.

      The fields of machine learning and optimization are highly interwoven. Optimization problems form the core of machine learning methods and modern optimization algorithms are using machine learning more and more to improve their efficiency.

      Machine learning has applications in all areas of science. There are many learning methods, each of which uses a different algorithmic structure to optimize predictions, based on the data received. Hence, the first objective of this book is to shed light on key principles and methods that are common within both fields.

      Machine learning and optimization share three components: representation, evaluation and iterative search. Yet while optimization solvers are generally designed to be fast and accurate on implicit models, machine learning methods need to be generic and trained offline on datasets. Machine learning problems present new challenges for optimization researchers, and machine learning practitioners seek simpler, generic optimization algorithms.

Quite recently, modern approaches to machine learning have also been applied to the design of optimization algorithms themselves, taking advantage of their ability to capture valuable information from complex structures in large spaces. Those capacities appear to be useful, especially for the representation and evaluation components. As large, complex structures are ubiquitous in optimization problems, and can be used as huge implicit datasets, the use of machine learning enabled the efficiency and genericity of optimization methods to be improved.

      This book presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. It is structured into two parts. Part 1 is dedicated to the most common optimization applications. Part 2 describes and implements several applications of machine learning.

      Part 1 comprises four chapters which focus on real-world application of optimization algorithms.

      Chapter 1 addresses the problem of vehicle routing with loading constraints and combines two combinatorial optimization problems: the capacity vehicle routing problem (CVRP) and the two-/three-dimensional bin packing problem (2/3D-BPP). The authors have studied real transport problems such as the transport of furniture or industrial machinery.

      The main objective of Chapter 2 is to create the most appropriate scheduling solution that optimizes several QoS metrics simultaneously; thus, the authors adapt the widely used metaheuristic, “Genetic Algorithm” as an optimization method. The proposed scheduling approach is tested by simulating a healthcare IoT application, modeled as a workflow and several scientific workflow benchmarks. The results show the effectiveness of the proposed approach; it generates a scheduling plan that better optimizes the various QoS metrics considered.

Chapter 3 focuses on the grey wolf optimization (GWO) and its adaptation to a continuous search space. It begins by addressing the mathematical modeling of optimization in a binary discrete search space. Binarization СКАЧАТЬ