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文件名称：Evolutionary Approach to Machine Learning and Deep Neural N. N-E and G R N
文件大小：18.14MB
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更新时间：2021-08-19 15:29:15
机器学习
Around 1990, I learned about GP (Genetic Programming) and developed interest in
this area shortly after learning about GAs (Genetic Algorithms). At that time, the
term GP was not yet established, and the concept was denoted by terms such as
structural GA and tree representation GA [1–3]. Since I had been researching AI so
far, I presumed that the only goal of GA was optimization (my opinion has changed
since), and I considered GA to be somewhat unsatisfactory. Consequently, as I
assumed that GA could not be used for handling knowledge representation, programs,
concept trees, and similar notions, I attempted to extend it. At exactly the
same time, when I presented my research to Dr. Philip D. Laird from NASA, who
was a Visiting Researcher at the Electrotechnical Laboratory. Dr. Laird is a
researcher in machine learning and is renowned for his book Learning from Good
and Bad Data [4]. He introduced me to the research of Prof. John Koza of Stanford
University. I still remember the excitement I felt while reading the technical reports
[5] written by Koza over the new year holidays. These reports were later compiled
in a massive volume that exceeded 800 pages [6]. Afterward, I stayed at Stanford
University in his laboratory and had a wonderful research life there.
Recently, an increasing number of researchers and students are specializing in
only GA and GP (and fields centered on them). Also, in comparison with the time
when AI and GA/GP were created, there are fewer and fewer researchers with
unique personalities. Although this is not in anyway disappointing, it cannot be
understood unless one has experienced the turmoil and the emotions associated with
the establishment of a new field. I expect such pleasant nervousness and excitement
to be maintained in this academic society as well, setting the path for the development
of a healthy community. This is one of my motivations in writing this book.