A Multitasks Learning Approach to Autonomous Agent based on Genetic Network Programming

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@InProceedings{Yang:2012:CEC,

• title = "A Multitasks Learning Approach to Autonomous Agent based on Genetic Network Programming",
• author = "Yang Yang and Shingo Mabu and Kotaro Hirasawa",
• pages = "1937--1943",
• booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation",
• year = "2012",
• editor = "Xiaodong Li",
• month = "10-15 " # jun,
• DOI = "doi:10.1109/CEC.2012.6256457",
• address = "Brisbane, Australia",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Genetic Network Programming, Evolutionary programming, Parallel and distributed algorithms",
• abstract = "The standard methodology in machine learning is to learn one problem at a time. But, many real-world problems are complex and have multitasks, and it is a bit hard to learn them well by one machine learning approach. So, the simultaneous learning of several tasks has been considered, that is, so-called multitask learning. This paper describes a new approach to the autonomous agent problem using the multitask learning scheme based on Genetic Network Programming (GNP), called ML-GNP, where each GNP is used to learn one corresponding task. MLGNP has some characteristics, such as distribution, interaction and autonomy, which are helpful for learning multitask problems. The experimental results illustrate that ML-GNP can give much better performance than learning all the tasks of the problem by one GNP algorithm.",
• notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.",

}

Genetic Programming entries for Yang Yang Shingo Mabu Kotaro Hirasawa

Citations