{"product_id":"construct-merge-solve-adapt-a-hybrid-metaheuristic-for-combinatorial-optimization-9783031601057","title":"Construct, Merge, Solve \u0026 Adapt: A Hybrid Metaheuristic for Combinatorial Optimization","description":"\u003cp\u003e • Author(s): Christian Blum\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Artificial Intelligence - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve \u0026amp; Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the tackled problem instance are generated in a probabilistic way. Hereby, each of these solutions is composed of a set of solution components. The components found in the generated solutions are then added to an initially empty sub-instance. Next, an exact solver is applied in order to compute the best solution of the sub-instance, which is then used to update the sub-instance provided as input for the next iteration. In this way, the power of exact solvers can be exploited for solving problem instances much too large for a standalone application of the solver.\u003c\/p\u003e \u003cp\u003eImportant research lines on CMSA from recent years are covered in this book. After an introductory chapter about standard CMSA, subsequent chapters cover a self-adaptive CMSA variant as well as a variant equipped with a learning component for improving the quality of the generated solutions over time. Furthermore, on outlining the advantages of using set-covering-based integer linear programming models for sub-instance solving, the author shows how to apply CMSA to problems naturally modelled by non-binary integer linear programming models. The book concludes with a chapter on topics such as the development of a problem-agnostic CMSA and the relation between large neighborhood search and CMSA. Combinatorial optimization problems used in the book as test cases include the minimum dominating set problem, the variable-sized bin packing problem, and an electric vehicle routing problem.\u003c\/p\u003e \u003cp\u003eThe book will be valuable and is intended for researchers, professionals and graduate students working in a wide range of fields, such as combinatorial optimization, algorithmics, metaheuristics, mathematical modeling, evolutionary computing, operations research, artificial intelligence, or statistics.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Paperback","offer_id":47658223992983,"sku":"9783031601057","price":10915.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9783031601057.webp?v=1775817935","url":"https:\/\/atlanticbooks.com\/products\/construct-merge-solve-adapt-a-hybrid-metaheuristic-for-combinatorial-optimization-9783031601057","provider":"Atlantic Books","version":"1.0","type":"link"}