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Bridging machine learning and evolutionary computation
Neurocomputing 146 (2014) 1
Contents lists available at ScienceDirect
Neurocomputing journal homepage: www.elsevier.com/locate/neucom
Editorial
Bridging machine learning and evolutionary computation
Machine learning is to discover patterns and rules from existing data, and predict future events. By nature, many machine learning problems can be modelled as optimization problems, often with more than one conflicting objective such as accuracy and complexity. It is also common that these problems have many locally optimal solutions. Traditional local optimization methods may not work well. For these reasons, evolutionary algorithms have been widely used as an optimization tool in the field of machine learning in recent years. On the other hand, ideas and techniques from machine learning can be used in and hybridized with evolutionary algorithms. This special issue is to provide a forum for state-of-the-art research on the interdisciplinary work between EAs and machine learning. It has accepted 11 papers after a rigorous review process in which each paper received at least three independent reviews. The first five papers are to use machine learning techniques to improve evolutionary algorithms. In the first paper, Lu et al. have proposed a self-adaptation scheme for differential evolution in which a surrogate model is used for selecting the most suitable parameters and strategies. The second paper by Martins et al. has investigated an estimation of distribution algorithm for the multidimensional knapsack problem and used machine learning techniques for discovering problem structure information. The third paper by Duro et al. has demonstrated how machine learning techniques can be used for decision support for many objective optimizations. The fourth paper by Ma et al. has employed opposition-based learning techniques for improving the performance of decomposition based multiobjective evolutionary algorithms. The fifth paper by Li et al. presents a general framework that uses advanced manifold learning techniques to capture the linear and non-linear geometric properties of the PS manifold. By on-line learning and adapting the approximated PS manifold, the
proposed framework is used for the reproduction of effective offspring. The last six papers apply evolutionary algorithms for solving machine learning problems. The paper by Fu et al. has investigated how to use evolutionary algorithms to solve an extreme learning machine problem. The paper by Xia et al. has proposed a multiobjective evolutionary algorithm for unsupervised feature selection in fault diagnosis. The paper by Dong et al. has studied how to determine feature weights in similarity-based clustering by using differential evolution. The paper by Ng et al. has used an evolutionary algorithm for improving a stock trading decision support system. The paper by Rosales-Perez et al. has adopted a multiobjective optimization evolutionary algorithm for dealing with a model type selection problem. The last paper by Antonwelli et al. has considered how to use a multiobjective optimization evolutionary algorithm for fuzzy classification for managing imbalanced datasets. The guest editors would like to thank all the authors for submitting their papers to this special issue, all the reviewers for their constructive reviews. They also thank the Editor-in-Chief, Professor Tom Heskes and the editor assistants for their great encouragement and help. Sam Kwong n City University of Hong Kong, Hong Kong
Qingfu Zhang City University of Hong Kong, Hong Kong University of Essex, UK Received 17 June 2014
n
http://dx.doi.org/10.1016/j.neucom.2014.06.051 0925-2312/& 2014 Elsevier B.V. All rights reserved.
Corresponding author.
Contents lists available at ScienceDirect
Neurocomputing journal homepage: www.elsevier.com/locate/neucom
Editorial
Bridging machine learning and evolutionary computation
Machine learning is to discover patterns and rules from existing data, and predict future events. By nature, many machine learning problems can be modelled as optimization problems, often with more than one conflicting objective such as accuracy and complexity. It is also common that these problems have many locally optimal solutions. Traditional local optimization methods may not work well. For these reasons, evolutionary algorithms have been widely used as an optimization tool in the field of machine learning in recent years. On the other hand, ideas and techniques from machine learning can be used in and hybridized with evolutionary algorithms. This special issue is to provide a forum for state-of-the-art research on the interdisciplinary work between EAs and machine learning. It has accepted 11 papers after a rigorous review process in which each paper received at least three independent reviews. The first five papers are to use machine learning techniques to improve evolutionary algorithms. In the first paper, Lu et al. have proposed a self-adaptation scheme for differential evolution in which a surrogate model is used for selecting the most suitable parameters and strategies. The second paper by Martins et al. has investigated an estimation of distribution algorithm for the multidimensional knapsack problem and used machine learning techniques for discovering problem structure information. The third paper by Duro et al. has demonstrated how machine learning techniques can be used for decision support for many objective optimizations. The fourth paper by Ma et al. has employed opposition-based learning techniques for improving the performance of decomposition based multiobjective evolutionary algorithms. The fifth paper by Li et al. presents a general framework that uses advanced manifold learning techniques to capture the linear and non-linear geometric properties of the PS manifold. By on-line learning and adapting the approximated PS manifold, the
proposed framework is used for the reproduction of effective offspring. The last six papers apply evolutionary algorithms for solving machine learning problems. The paper by Fu et al. has investigated how to use evolutionary algorithms to solve an extreme learning machine problem. The paper by Xia et al. has proposed a multiobjective evolutionary algorithm for unsupervised feature selection in fault diagnosis. The paper by Dong et al. has studied how to determine feature weights in similarity-based clustering by using differential evolution. The paper by Ng et al. has used an evolutionary algorithm for improving a stock trading decision support system. The paper by Rosales-Perez et al. has adopted a multiobjective optimization evolutionary algorithm for dealing with a model type selection problem. The last paper by Antonwelli et al. has considered how to use a multiobjective optimization evolutionary algorithm for fuzzy classification for managing imbalanced datasets. The guest editors would like to thank all the authors for submitting their papers to this special issue, all the reviewers for their constructive reviews. They also thank the Editor-in-Chief, Professor Tom Heskes and the editor assistants for their great encouragement and help. Sam Kwong n City University of Hong Kong, Hong Kong
Qingfu Zhang City University of Hong Kong, Hong Kong University of Essex, UK Received 17 June 2014
n
http://dx.doi.org/10.1016/j.neucom.2014.06.051 0925-2312/& 2014 Elsevier B.V. All rights reserved.
Corresponding author.