Variants of Evolutionary Algorithms for Real-World Applications

1. Front Matter

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2. Section I: Introduction

   1. Evolutionary Optimization

      • Christian Blum, Raymond Chiong, Maurice Clerc, Kenneth De Jong, Zbigniew Michalewicz, Ferrante Neri et al.

      Pages 1-29

3. Section II: Planning and Scheduling

   1. An Evolutionary Approach to Practical Constraints in Scheduling: A Case-Study of the Wine Bottling Problem

      • Arvind Mohais, Sven Schellenberg, Maksud Ibrahimov, Neal Wagner, Zbigniew Michalewicz

      Pages 31-58

   2. A Memetic Framework for Solving the Lot Sizing and Scheduling Problem in Soft Drink Plants

      • Claudio F. M. Toledo, Marcio S. Arantes, Paulo M. França, Reinaldo Morabito

      Pages 59-93

   3. Simulation-Based Evolutionary Optimization of Complex Multi-Location Inventory Models

      • Jörg Lässig, Christian A. Hochmuth, Stefanie Thiem

      Pages 95-141

   4. A Fuzzy-Evolutionary Approach to the Problem of Optimisation and Decision-Support in Supply Chain Networks

      • Sven Schellenberg, Arvind Mohais, Maksud Ibrahimov, Neal Wagner, Zbigniew Michalewicz

      Pages 143-166

   5. A Genetic-Based Solution to the Task-Based Sailor Assignment Problem

      • Dipankar Dasgupta, Deon Garrett, Fernando Nino, Alex Banceanu, David Becerra

      Pages 167-203

   6. Genetic Algorithms for Manufacturing Process Planning

      • Guohua Ma, Fu Zhang

      Pages 205-244

4. Section III: Engineering

   1. A Fitness Granulation Approach for Large-Scale Structural Design Optimization

      • Mohsen Davarynejad, Jos Vrancken, Jan van den Berg, Carlos A. Coello Coello

      Pages 245-280

   2. A Reinforcement Learning Based Hybrid Evolutionary Algorithm for Ship Stability Design

      • Osman Turan, Hao Cui

      Pages 281-303

   3. An Interactively Constrained Neuro-Evolution Approach for Behavior Control of Complex Robots

      • Christian Rempis, Frank Pasemann

      Pages 305-341

5. Section IV: Data Collection, Retrieval and Mining

   1. A Genetic Programming-Based Approach for the Performance Characteristics Assessment of Stabilized Soil

      • Amir Hossein Alavi, Amir Hossein Gandomi, Ali Mollahasani

      Pages 343-376

   2. Evolving Cellular Neural Networks for the Automated Segmentation of Multiple Sclerosis Lesions

      • Eleonora Bilotta, Antonio Cerasa, Pietro Pantano, Aldo Quattrone, Andrea Staino, Francesca Stramandinoli

      Pages 377-412

   3. An Evolutionary Algorithm for Skyline Query Optimization

      • Marlene Goncalves, Ivette Martínez, Gabi Escuela, Fabiola Di Bartolo, Francelice Sardá

      Pages 413-436

   4. A Bio-inspired Approach to Self-organization of Mobile Nodes in Real-Time Mobile Ad Hoc Network Applications

      • Cem Şafak Şahin, Elkin Urrea, M. Ümit Uyar, Stephen Gundry

      Pages 437-462

6. Back Matter

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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.

• Differential Evolution
• Evolution Strategies
• Evolutionary Programming
• Genetic Algorithms
• Genetic Programming

From the reviews:

“This handbook offers a comprehensive introduction to advanced ways to adapt EAs to complex real-world problems. These applications include production lines, scheduling, supply networks, engineering optimization, ship design, and soil properties. Considering the breadth of the applications and depth of the discussions, this compendium is a welcome addition to the EA literature.” (R. Goldberg, Computing Reviews, June, 2013)

• Faculty of ICT, Swinburne University of Technology, Melbourne, Australia

  Raymond Chiong

• Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, China

  Thomas Weise

• School of Computer Science, University of Adelaide, Adelaide, Australia

  Zbigniew Michalewicz

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