Download A Connectionist Machine for Genetic Hillclimbing by David Ackley PDF

By David Ackley

ISBN-10: 1461291925

ISBN-13: 9781461291923

ISBN-10: 1461319978

ISBN-13: 9781461319979

In the "black field functionality optimization" challenge, a seek technique is needed to discover an extremal element of a functionality with out realizing the constitution of the functionality or the variety of attainable functionality values. fixing such difficulties successfully calls for talents. at the one hand, a technique has to be able to studying whereas looking: It needs to assemble international information regarding the gap and focus the quest within the so much promising areas. nevertheless, a method has to be in a position to sustained exploration: If a seek of the main promising sector doesn't discover a passable aspect, the tactic needs to redirect its efforts into different areas of the gap. This dissertation describes a connectionist studying computing device that produces a seek process known as stochastic iterated genetic hillclimb­ ing (SIGH). seen over a quick time period, SIGH monitors a coarse-to-fine looking out procedure, like simulated annealing and genetic algorithms. even if, in SIGH the convergence method is reversible. The connectionist implementation makes it attainable to diverge the quest after it has converged, and to get well coarse-grained informa­ tion concerning the house that was once suppressed in the course of convergence. The profitable optimization of a posh functionality via SIGH often in­ volves a sequence of such converge/diverge cycles.

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Extra resources for A Connectionist Machine for Genetic Hillclimbing

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The minimum cut partitioning problem for this graph is to divide the twelve nodes into two groups of six such that the number of edges connecting nodes in different groups is minimized. evaluated, should there be a desire to do so. Hamming distance is useful as a crude measure of similarity between points, representing the weak assumption that all of the dimensions are equally important in determining similarity. Small binary spaces can be visualized. A one dimensional space corresponds to the ends of a line segment; a two dimensional space corresponds to the corners of a square; a three dimensional space corresponds to the corners of a cube.

Such a definition would amount to an assumption that there are "ridges" in the search space: If we are given two good points, we can guess that the reason they are both good is that they are lying at different points on a ridge in the space. D nder such an assumption, it would be rational to search on the line through the points, both interpolating between them and extrapolating beyond them, in hopes of finding even better points elsewhere on the presumed ridge. In spaces with coarse-grained dimensions the situation is somewhat more complicated.

In this dissertation, the learning system is required to do without any such guidance from above. To get a little bit ahead of the story, in SIGH, the standard of comparison problem is solved basically by defining "good" behavior to be generating higher function values compared to recent history. 1 Knowledge representation. A search strategy can be characterized The model 31 by three components. First, it may possess ongoing state-some kind of knowledge representation that contains a distillation of the information previously acquired during the search, and provides a means for feeding information forward from the past to the future.

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A Connectionist Machine for Genetic Hillclimbing by David Ackley


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