Check Swinburne's subscriptions for full text availabilitySearch Swinburne Research Bank
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/231393
- Title
- Variants of evolutionary algorithms for real-world applications
- Author(s)
- Chiong, Raymond; Weise, Thomas; Michalewicz, Zbigniew
- Abstract
- Started as a mere academic curiosity, Evolutionary Algorithms (EAs) first came into sight back in the 1960s. However, it was not until the 1980s that the research on EAs became less theoretical and more practical. As a manifestation of population-based, stochastic search algorithms that mimic natural evolution, EAs use genetic operators such as crossover and mutation for the search process to generate new solutions through a repeated application of variation and selection. 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. The general-purpose, black-box character of EAs makes them suitable for a wide range of realworld applications. Standard EAs such as Genetic Algorithms (GAs) and Genetic Programming (GP) are becoming more and more accepted in the industry and commercial sectors. With the dramatic increase in computational power today, an incredible diversification of new application areas of these techniques can be observed. At the same time, variants and other classes of evolutionary optimisation methods such as Differential Evolution, Estimation of Distribution Algorithms, Co-evolutionary Algorithms and Multi-Objective Evolutionary Algorithms (MOEAs) have been developed. When applications or systems utilising EAs reach the production stage, off-the-shelf versions of these methods are typically replaced by dedicated algorithm variants. These specialised EAs often use tailored reproduction operators, search spaces differing significantly from the well-known binary or tree-based encodings, non-trivial genotype-phenotype mappings, or are hybridised with other optimisation algorithms. This book aims to promote the practitioner's view on EAs by giving a comprehensive discussion of how EAs can be adapted to the requirements of various applications in real-world domains.
- Publication type
- Book
- Research centre
- Swinburne University of Technology. Faculty of Information and Communication Technologies
- Publication year
- 2012
- Keyword(s)
- Data mining; Distribution algorithms; Evolutionary algorithms
- Publisher
- Springer
- ISBN
- 9783642234231, 3642234232
- Publisher URL
- http://dx.doi.org/10.1007/978-3-642-23424-8
- Copyright
- Copyright © Springer-Verlag Berlin Heidelberg.
- Peer reviewed
