Abstract
This dissertation describes, demonstrates, and analyzes a new search technique called stochastic iterated genetic hillclimbing (SIGH). SIGH performs function optimizations in high-dimensional, binary vector spaces. Although the technique can be characterized in abstract computational terms, it emerged from research into massively parallel connectionist learning machines, and it has a compact implementation in a connectionist architecture. The behavior generated by the machine displays elements of two existing search techniques—stochastic hillclimbing and genetic search.
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© 1987 Kluwer Academic Publishers
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Ackley, D.H. (1987). Introduction. In: A Connectionist Machine for Genetic Hillclimbing. The Kluwer International Series in Engineering and Computer Science, vol 28. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1997-9_1
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DOI: https://doi.org/10.1007/978-1-4613-1997-9_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-9192-3
Online ISBN: 978-1-4613-1997-9
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