- title = "On synchronized evolution of the network of automata",
- author = "Yoshiyuki Inagaki",
- year = "1999",
- description = "Thesis (Ph. D., Social Science)--University of California, Irvine, 1999.; Includes bibliographical references (leaves 97-98).",
- oai = "oai:xtcat.oclc.org:OCLCNo/ocm43628471",
- school = "Social Science, University of California, Irvine",
- address = "USA",
- keywords = "genetic algorithms, genetic programming, Computer science, Sequential machine theory, Artificial intelligence",
-
URL = "
https://uci.primo.exlibrisgroup.com/discovery/fulldisplay?docid=alma991023376589704701&context=L&vid=01CDL_IRV_INST:UCI&search_scope=MyInst_and_CI&tab=Everything&lang=en",
-
URL = "http://search.proquest.com/docview/304498722",
- size = "112 pages",
- abstract = "One of the tasks in machine learning is to build a device which guesses each next input symbol of a sequence as it takes one input symbol from the sequence. We studied new approaches to this task. We suggest that deterministic finite automata, DFA , are good building blocks for this device together with genetic algorithms, GA , which let these automata {"}evolve{"} to guess each next input symbol of the sequence. Moreover, we studied the way to combine these highly fit automata so that the network of them would compensate for each other's weakness and guess better than any single automaton can. We studied the simplest approaches to combine automata: building trees of automata with special purpose automata, which may be called switch-boards . These switch-board automata are located on the internal nodes of the tree, take an input symbol from the input sequence just like other automata do, and guess which subtree will make a right guess on each next input symbol. Genetic algorithms again play a crucial role in searching for switch-board automata. We studied various ways of growing trees of automata and tested them on sample input sequences, mainly note pitches, note duration, and up/down notes of Bach's Fugue. The test results show that DFAs together with GAs seem to be very effective for this type of pattern learning task. Besides this main finding, the tests revealed several interesting things. For example, the sequence of the note pitches is more predictable than the sequence of up/down notes. This is counter intuitive. Larger alphabets mean larger numbers of possible configurations of automata. This implies a larger search space for genetic algorithms; therefore, the algorithms should have difficulty finding automata which fit the tasks. However, the tree devices built to predict the note pitches often outperform those built to predict the up/down notes even though the size of the input alphabet for the former is 8 and that for the latter is 2. This suggests the following: The genetic search is so powerful and effective that if there are good solutions in its search space, it will find one when it works with a large enough population for a large enough number of generations. Therefore, if the search fails to find a good solution, the search space almost certainly does not contain one.",
-
notes = "OCLC : (OCoLC)43628471 UCI Langson library LD
791.9.S56 1999
UMI 9940706 Supervisor Louis Narens
See also \cite{inagaki:2002:TEC}",
