What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{daida:2001:GPEM,
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author = "Jason M. Daida and Robert R. Bertram and
Stephen A. Stanhope and Jonathan C. Khoo and
Shahbaz A. Chaudhary and Omer A. Chaudhri and John A. {Polito II}",
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title = "What Makes a Problem {GP}-Hard? Analysis of a Tunably
Difficult Problem in Genetic Programming",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2001",
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volume = "2",
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number = "2",
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pages = "165--191",
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month = jun,
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keywords = "genetic algorithms, genetic programming, problem
difficulty, test problems, fitness landscapes, GP
theory",
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ISSN = "1389-2576",
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broken = "http://ipsapp009.lwwonline.com/content/getfile/4723/5/5/fulltext.pdf",
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DOI = "doi:10.1023/A:1011504414730",
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abstract = "This paper addresses the issue of what makes a problem
genetic programming (GP)-hard by considering the
binomial-3 problem. In the process, we discuss the
efficacy of the metaphor of an adaptive fitness
landscape to explain what is GP-hard. We indicate that,
at least for this problem, the metaphor is
misleading.",
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notes = "patched lilgp. Mersenne Twister. Size and Shape of
solutions to 3 binomial - tunably difficult by changing
random constants used. Edvard Munch Scream.
Inconsistency of ERC value within parse tree context.
Destructive crossover. P180 {"}the fitness function did
not need to be rugged for GP to encounter
difficulty.{"} GP as error correcting. Mathematica.
p186 {"}increased population meant more individuals
gathered around the{"} suboptimal {"}0.8
attractor{"}.
Article ID: 335714",
- }
Genetic Programming entries for
Jason M Daida
Robert R Bertram
Stephen A Stanhope
Jonathan C Khoo
Shahbaz A Chaudhary
Omar A Chaudhri
John A Polito 2
Citations