Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Analyzing active interactive genetic algorithms using visual analytics
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Extracting user preferences by GTM for aiGA weight tuning in unit selection text-to-speech synthesis
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Interactive genetic algorithms with large population and semi-supervised learning
Applied Soft Computing
| Hi-index | 0.00 |
Since their inception, active interactive genetic algorithms have successfully combat user evaluation fatigue induced by repetitive evaluation. Their success originates on building models of the user preferences based on partial-order graphs to create a numeric synthetic fitness. Active interactive genetic algorithms can easily reduce up to seven times the number of evaluations required from the user by optimizing such a synthetic fitness. However, despite basic understanding of the underlying mechanisms there is still a lack of principled understanding of what properties make a partial ordering graph a successful model of user preferences. Also, there has been little research conducted about how to integrate together the contributions of different users to successfully capitalize on parallelized evaluation schemes. This paper addresses both issues describing: (1) what properties make a partial-order graph successful and accurate, and (2) how partial-order graphs obtained from different users can be merged meaningfully.