IEEE 2018 World Congress on Computational Intelligence (WCCI 2018) Special Session:
Computational Intelligence for the Automated Design of Machine Learning and Search (CIAD 2018)
Aims, Scope and List of Topics:
Machine learning and search algorithms play an imperative role in solving real world problems in industry and business sectors. Systems employing these techniques have contributed to many facets of industry including data mining, transportation, health systems, computer vision, computer security, robotics, software engineering and scheduling amongst others. These systems employ one or more techniques such as neural networks, fuzzy logic, evolutionary algorithms, multi-agent approaches and rule-based systems. Implementation of these techniques require a number of design decisions to be made, e.g. what architecture to use, what parameter values to use, and derivation of problem specific operators. It may also be necessary to employ a hybrid system combining techniques to solve a problem which introduces additional decisions such as which techniques to use and how to combine these techniques. This makes the development of computational systems time consuming, requiring many person-hours. Consequently, there have been a number of initiatives to automate these processes using computational intelligence.
There has been a fair amount of research into parameter tuning and control. The field of auto-ML aims to automate the design of machine learning algorithms so as to produce off-the-shelf machine learning techniques. Attempts to automate neural network architecture design has led to the field of neuroevolution. Research in this area has also been directed at inducing fuzzy functions, rule-based systems and multi-agent architectures. Hyper-heuristics, which were initially aimed at providing generalized solutions to combinatorial optimization problems, are shown to be effective in the automated design of search techniques. Evolutionary algorithms such as genetic programming and genetic algorithms have made a valuable contribution to this field. The aim of this special session is to examine and promote recent developments in the field and future directions including the challenges and how these can be overcome.
The topics covered include, but are not limited to the following:
• Architecture design, e.g. design of neural networks and multi-agent architectures
• Automated hybridization of intelligent techniques
• Auto-ML
• Automatic programming
• Derivation of constructive heuristics
• Derivation of evaluation functions
• Derivation of operators
• Explainable machine learning
• Hyper-heuristics
• Neuroevolution
• Parameter control and tuning
• Search-based software engineering
• Self*-search
Organizers:
Nelishia Pillay,
University of Pretoria, South Africa
E-mail: npillay@cs.up.ac.za
Rong Qu,
University of Nottingham, UK
E-mail: Rong.Qu@nottingham.ac.uk
Important Dates:
Paper submission deadline: 15 January, 2018
Paper acceptance notification: 15 March, 2018
Final paper submission deadline: 1 May, 2018
Early registration: 1 May, 2018
Paper Submission:
Special session papers are treated the same as regular papers and must be submitted via the WCCI 2018 submission website. When submitting choose the " Computational Intelligence for the Automated Design of Machine Learning and Search " special session from the "Main Research Topic" list.
Aims, Scope and List of Topics:
Machine learning and search algorithms play an imperative role in solving real world problems in industry and business sectors. Systems employing these techniques have contributed to many facets of industry including data mining, transportation, health systems, computer vision, computer security, robotics, software engineering and scheduling amongst others. These systems employ one or more techniques such as neural networks, fuzzy logic, evolutionary algorithms, multi-agent approaches and rule-based systems. Implementation of these techniques require a number of design decisions to be made, e.g. what architecture to use, what parameter values to use, and derivation of problem specific operators. It may also be necessary to employ a hybrid system combining techniques to solve a problem which introduces additional decisions such as which techniques to use and how to combine these techniques. This makes the development of computational systems time consuming, requiring many person-hours. Consequently, there have been a number of initiatives to automate these processes using computational intelligence.
There has been a fair amount of research into parameter tuning and control. The field of auto-ML aims to automate the design of machine learning algorithms so as to produce off-the-shelf machine learning techniques. Attempts to automate neural network architecture design has led to the field of neuroevolution. Research in this area has also been directed at inducing fuzzy functions, rule-based systems and multi-agent architectures. Hyper-heuristics, which were initially aimed at providing generalized solutions to combinatorial optimization problems, are shown to be effective in the automated design of search techniques. Evolutionary algorithms such as genetic programming and genetic algorithms have made a valuable contribution to this field. The aim of this special session is to examine and promote recent developments in the field and future directions including the challenges and how these can be overcome.
The topics covered include, but are not limited to the following:
• Architecture design, e.g. design of neural networks and multi-agent architectures
• Automated hybridization of intelligent techniques
• Auto-ML
• Automatic programming
• Derivation of constructive heuristics
• Derivation of evaluation functions
• Derivation of operators
• Explainable machine learning
• Hyper-heuristics
• Neuroevolution
• Parameter control and tuning
• Search-based software engineering
• Self*-search
Organizers:
Nelishia Pillay,
University of Pretoria, South Africa
E-mail: npillay@cs.up.ac.za
Rong Qu,
University of Nottingham, UK
E-mail: Rong.Qu@nottingham.ac.uk
Important Dates:
Paper submission deadline: 15 January, 2018
Paper acceptance notification: 15 March, 2018
Final paper submission deadline: 1 May, 2018
Early registration: 1 May, 2018
Paper Submission:
Special session papers are treated the same as regular papers and must be submitted via the WCCI 2018 submission website. When submitting choose the " Computational Intelligence for the Automated Design of Machine Learning and Search " special session from the "Main Research Topic" list.
Aims, Scope and List of Topics:
Machine learning and search algorithms play an imperative role in solving real world problems in industry and business sectors. Systems employing these techniques have contributed to many facets of industry including data mining, transportation, health systems, computer vision, computer security, robotics, software engineering and scheduling amongst others. These systems employ one or more techniques such as neural networks, fuzzy logic, evolutionary algorithms, multi-agent approaches and rule-based systems. Implementation of these techniques require a number of design decisions to be made, e.g. what architecture to use, what parameter values to use, and derivation of problem specific operators. It may also be necessary to employ a hybrid system combining techniques to solve a problem which introduces additional decisions such as which techniques to use and how to combine these techniques. This makes the development of computational systems time consuming, requiring many person-hours. Consequently, there have been a number of initiatives to automate these processes using computational intelligence.
There has been a fair amount of research into parameter tuning and control. The field of auto-ML aims to automate the design of machine learning algorithms so as to produce off-the-shelf machine learning techniques. Attempts to automate neural network architecture design has led to the field of neuroevolution. Research in this area has also been directed at inducing fuzzy functions, rule-based systems and multi-agent architectures. Hyper-heuristics, which were initially aimed at providing generalized solutions to combinatorial optimization problems, are shown to be effective in the automated design of search techniques. Evolutionary algorithms such as genetic programming and genetic algorithms have made a valuable contribution to this field. The aim of this special session is to examine and promote recent developments in the field and future directions including the challenges and how these can be overcome.
The topics covered include, but are not limited to the following:
• Architecture design, e.g. design of neural networks and multi-agent architectures
• Automated hybridization of intelligent techniques
• Auto-ML
• Automatic programming
• Derivation of constructive heuristics
• Derivation of evaluation functions
• Derivation of operators
• Explainable machine learning
• Hyper-heuristics
• Neuroevolution
• Parameter control and tuning
• Search-based software engineering
• Self*-search
Organizers:
Nelishia Pillay,
University of Pretoria, South Africa
E-mail: npillay@cs.up.ac.za
Rong Qu,
University of Nottingham, UK
E-mail: Rong.Qu@nottingham.ac.uk
Important Dates:
Paper submission deadline: 15 January, 2018
Paper acceptance notification: 15 March, 2018
Final paper submission deadline: 1 May, 2018
Early registration: 1 May, 2018
Paper Submission:
Special session papers are treated the same as regular papers and must be submitted via the WCCI 2018 submission website. When submitting choose the " Computational Intelligence for the Automated Design of Machine Learning and Search " special session from the "Main Research Topic" list.
Aims, Scope and List of Topics:
Machine learning and search algorithms play an imperative role in solving real world problems in industry and business sectors. Systems employing these techniques have contributed to many facets of industry including data mining, transportation, health systems, computer vision, computer security, robotics, software engineering and scheduling amongst others. These systems employ one or more techniques such as neural networks, fuzzy logic, evolutionary algorithms, multi-agent approaches and rule-based systems. Implementation of these techniques require a number of design decisions to be made, e.g. what architecture to use, what parameter values to use, and derivation of problem specific operators. It may also be necessary to employ a hybrid system combining techniques to solve a problem which introduces additional decisions such as which techniques to use and how to combine these techniques. This makes the development of computational systems time consuming, requiring many person-hours. Consequently, there have been a number of initiatives to automate these processes using computational intelligence.
There has been a fair amount of research into parameter tuning and control. The field of auto-ML aims to automate the design of machine learning algorithms so as to produce off-the-shelf machine learning techniques. Attempts to automate neural network architecture design has led to the field of neuroevolution. Research in this area has also been directed at inducing fuzzy functions, rule-based systems and multi-agent architectures. Hyper-heuristics, which were initially aimed at providing generalized solutions to combinatorial optimization problems, are shown to be effective in the automated design of search techniques. Evolutionary algorithms such as genetic programming and genetic algorithms have made a valuable contribution to this field. The aim of this special session is to examine and promote recent developments in the field and future directions including the challenges and how these can be overcome.
The topics covered include, but are not limited to the following:
• Architecture design, e.g. design of neural networks and multi-agent architectures
• Automated hybridization of intelligent techniques
• Auto-ML
• Automatic programming
• Derivation of constructive heuristics
• Derivation of evaluation functions
• Derivation of operators
• Explainable machine learning
• Hyper-heuristics
• Neuroevolution
• Parameter control and tuning
• Search-based software engineering
• Self*-search
Organizers:
Nelishia Pillay,
University of Pretoria, South Africa
E-mail: npillay@cs.up.ac.za
Rong Qu,
University of Nottingham, UK
E-mail: Rong.Qu@nottingham.ac.uk
Important Dates:
Paper submission deadline: 15 January, 2018
Paper acceptance notification: 15 March, 2018
Final paper submission deadline: 1 May, 2018
Early registration: 1 May, 2018
Paper Submission:
Special session papers are treated the same as regular papers and must be submitted via the WCCI 2018 submission website. When submitting choose the " Computational Intelligence for the Automated Design of Machine Learning and Search " special session from the "Main Research Topic" list.
Aims, Scope and List of Topics:
Machine learning and search algorithms play an imperative role in solving real world problems in industry and business sectors. Systems employing these techniques have contributed to many facets of industry including data mining, transportation, health systems, computer vision, computer security, robotics, software engineering and scheduling amongst others. These systems employ one or more techniques such as neural networks, fuzzy logic, evolutionary algorithms, multi-agent approaches and rule-based systems. Implementation of these techniques require a number of design decisions to be made, e.g. what architecture to use, what parameter values to use, and derivation of problem specific operators. It may also be necessary to employ a hybrid system combining techniques to solve a problem which introduces additional decisions such as which techniques to use and how to combine these techniques. This makes the development of computational systems time consuming, requiring many person-hours. Consequently, there have been a number of initiatives to automate these processes using computational intelligence. There has been a fair amount of research into parameter tuning and control. The field of auto-ML aims to automate the design of machine learning algorithms so as to produce off-the-shelf machine learning techniques. Attempts to automate neural network architecture design has led to the field of neuroevolution. Research in this area has also been directed at inducing fuzzy functions, rule-based systems and multi-agent architectures. Hyper-heuristics, which were initially aimed at providing generalized solutions to combinatorial optimization problems, are shown to be effective in the automated design of search techniques. Evolutionary algorithms such as genetic programming and genetic algorithms have made a valuable contribution to this field. The aim of this special session is to examine and promote recent developments in the field and future directions including the challenges and how these can be overcome.
The topics covered include, but are not limited to the following:
• Architecture design, e.g. design of neural networks and multi-agent architectures
• Automated hybridization of intelligent techniques
• Auto-ML
• Automatic programming
• Derivation of constructive heuristics
• Derivation of evaluation functions
• Derivation of operators
• Explainable machine learning
• Hyper-heuristics
• Neuroevolution
• Parameter control and tuning
• Search-based software engineering
• Self*-search
Organizers:
Nelishia Pillay,
University of Pretoria, South Africa
E-mail: npillay@cs.up.ac.za
Rong Qu,
University of Nottingham, UK
E-mail: Rong.Qu@nottingham.ac.uk
Important Dates:
Paper submission deadline: 1 February 2018 (extended deadline)
Paper acceptance notification: 15 March, 2018
Final paper submission deadline: 1 May, 2018
Early registration: 1 May, 2018
Paper Submission:
Special session papers are treated the same as regular papers and must be submitted via the WCCI 2018 submission website. When submitting choose the " Computational Intelligence for the Automated Design of Machine Learning and Search " special session from the "Main Research Topic" list.