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{ "item_title" : "Data-Driven Evolutionary Optimization", "item_author" : [" Yaochu Jin", "Handing Wang", "Chaoli Sun "], "item_description" : "Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as 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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/03/074/642/3030746429_b.jpg", "price_data" : { "retail_price" : "199.99", "online_price" : "199.99", "our_price" : "199.99", "club_price" : "199.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Data-Driven Evolutionary Optimization|Yaochu Jin

Data-Driven Evolutionary Optimization : Integrating Evolutionary Computation, Machine Learning and Data Science

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Overview

Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available.

This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as 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.

This item is Non-Returnable

Details

  • ISBN-13: 9783030746421
  • ISBN-10: 3030746429
  • Publisher: Springer
  • Publish Date: June 2022
  • Dimensions: 9.21 x 6.14 x 0.86 inches
  • Shipping Weight: 1.29 pounds
  • Page Count: 393

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