Variants of evolutionary algorithms for real-world applications
Material type: TextPublication details: Springer 2011 BerlinDescription: xiv, 462 p. With indexISBN:- 9783642234231
- 006.31 V2
Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|
Book | Ahmedabad General Stacks | Non-fiction | 006.31 V2 (Browse shelf(Opens below)) | Available | 197815 |
Browsing Ahmedabad shelves, Shelving location: General Stacks, Collection: Non-fiction Close shelf browser (Hides shelf browser)
006.31 R6F4 A first course in machine learning | 006.31 S3D3 The deep learning revolution | 006.31 S8R3 Reinforcement learning: an introduction | 006.31 V2 Variants of evolutionary algorithms for real-world applications | 006.31 W2M2 Machine learning refined: foundations, algorithms, and applications | 006.3101 W3E9 Everyday chaos: technology, complexity, and how we’re thriving in a new world of possibility | 006.31015195 A7C6 A computational approach to statistical learning |
Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book “Variants of Evolutionary Algorithms for Real-World Applications” aims to promote the practitioner’s view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the real-world domains. It comprises 14 chapters, including an introductory chapter re-visiting the fundamental question of what an EA is and other chapters addressing a range of real-world problems such as production process planning, inventory system and supply chain network optimisation, task-based jobs assignment, planning for CNC-based work piece construction, mechanical/ship design tasks that involve runtime-intense simulations, data mining for the prediction of soil properties, automated tissue classification for MRI images, and database query optimisation, among others. These chapters demonstrate how different types of problems can be successfully solved using variants of EAs and how the solution approaches are constructed, in a way that can be understood and reproduced with little prior knowledge on optimisation.
https://www.springer.com/gp/book/9783642234231
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