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Evolutionary Algorithms (EAs) are populationbased, 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 RealWorld Applications aims to promote the practitioners view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the realworld domains. It comprises 14 chapters, including an introductory chapter revisiting the fundamental question of what an EA is and other chapters addressing a range of realworld problems such as production process planning, inventory system and supply chain network optimisation, taskbased jobs assignment, planning for CNCbased work piece construction, mechanical/ship design tasks that involve runtimeintense 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.
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