An Introduction to Genetic Algorithms
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Cites methods from "An Introduction to Genetic Algorith..."
...the specialized Web crawler or “spider” of Menczer et al. [280, 281]. This is a program that performs a Web crawl to find results for a particular query. The method used is a type of genetic algorithm [285] or enrichment method [180] that in its simplest form has a number of “agents” that start crawling the Web at random, looking for pages that contain, for example, particular words or sets of words giv...
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"An Introduction to Genetic Algorith..." refers background or methods in this paper
...…about the form of the learning rule came in part from the fact that a known good learning rule for such networks—the "Widrow−Hoff" or "delta" rule—has the form Chapter 2: Genetic Algorithms in Problem Solving 58 (Rumelhart et al. 1986), where n is a constant representing the learning rate....
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...It is known that single−layer networks can learn only those classes of input−output mappings that are "linearly separable" (Rumelhart et al. 1986)....
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...For the very ambitious reader: Compare the performance of the GA with that of back−propagation (Rumelhart, Hinton, and Williams 1986a) in the same way that Montana and Davis did....
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...(For overviews of neural networks and their applications, see Rumelhart et al. 1986, McClelland et al. 1986, and Hertz, Krogh, and Palmer 1991.)...
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...In the back−propagation learning procedure (Rumelhart, Hinton, and Williams 1986), after each input has propagated through the network and an output has been produced, a "teacher" compares the activation value at each output unit with the correct values, and the weights in the network are adjusted…...
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"An Introduction to Genetic Algorith..." refers background or methods or result in this paper
...Though five problems is not many for such a comparison in view of the number of problems on which GP has been tried, these results bring into question the claim (Koza 1992) that the crossover operator is a major contributor to GP's success....
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...John Koza (1992) also applied the GP paradigm to evolve CAs for simple random−number generation....
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...John Koza (1992,1994) has used a form of the genetic algorithm to evolve Lisp programs to perform various tasks....
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...Koza (1992) discusses how to amend the fitness function to produce a more efficient program to do this task....
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...Implement a genetic programming algorithm and use it to solve the "6−multiplexer" problem (Koza 1992)....
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