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.
Author(s): Christian Blum, Raymond Chiong, Maurice Clerc, Kenneth De Jong, Zbigniew Michalewicz (auth.), Raymond Chiong, Thomas Weise, Zbigniew Michalewicz (eds.)
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2012
Language: English
Pages: 466
Tags: Computational Intelligence; Artificial Intelligence (incl. Robotics)
Front Matter....Pages -
Evolutionary Optimization....Pages 1-29
An Evolutionary Approach to Practical Constraints in Scheduling: A Case-Study of the Wine Bottling Problem....Pages 31-58
A Memetic Framework for Solving the Lot Sizing and Scheduling Problem in Soft Drink Plants....Pages 59-93
Simulation-Based Evolutionary Optimization of Complex Multi-Location Inventory Models....Pages 95-141
A Fuzzy-Evolutionary Approach to the Problem of Optimisation and Decision-Support in Supply Chain Networks....Pages 143-166
A Genetic-Based Solution to the Task-Based Sailor Assignment Problem....Pages 167-203
Genetic Algorithms for Manufacturing Process Planning....Pages 205-244
A Fitness Granulation Approach for Large-Scale Structural Design Optimization....Pages 245-280
A Reinforcement Learning Based Hybrid Evolutionary Algorithm for Ship Stability Design....Pages 281-303
An Interactively Constrained Neuro-Evolution Approach for Behavior Control of Complex Robots....Pages 305-341
A Genetic Programming-Based Approach for the Performance Characteristics Assessment of Stabilized Soil....Pages 343-376
Evolving Cellular Neural Networks for the Automated Segmentation of Multiple Sclerosis Lesions....Pages 377-412
An Evolutionary Algorithm for Skyline Query Optimization....Pages 413-436
A Bio-inspired Approach to Self-organization of Mobile Nodes in Real-Time Mobile Ad Hoc Network Applications....Pages 437-462
Back Matter....Pages -
