A Linear Structured Approach and A Refined Fitness Function in Genetic Programming for Multi-class Object Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{zhang:2007:CS,
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author = "Mengjie Zhang and Christopher Graeme Fogelberg and
Yuejin Ma",
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title = "A Linear Structured Approach and A Refined Fitness
Function in Genetic Programming for Multi-class Object
Classification",
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journal = "Connection Science",
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year = "2007",
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volume = "19",
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number = "4",
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pages = "339--359",
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note = "Special Issue: Evolutionary Learning and
Optimisation",
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keywords = "genetic algorithms, genetic programming, Linear
genetic programming, Program structure, Program
representation, Fitness function, Multi-class
classification, Object classification, Object
recognition",
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ISSN = "0954-0091",
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DOI = "doi:10.1080/09540090701725557",
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size = "21 pages",
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abstract = "This paper describes an approach to the use of genetic
programming (GP) to multi-class object recognition
problems. Rather than using the standard tree
structures to represent evolved classifier programs
which only produce a single output value that must be
further translated into a set of class labels, this
approach uses a linear structure to represent evolved
programs, which use multiple target registers each for
a single class. The simple error rate fitness function
is refined and a new fitness function is introduced to
approximate the true feature space of an object
recognition problem. This approach is examined and
compared with the tree based GP on three data sets
providing object recognition problems of increasing
difficulty. The results show that this approach
outperforms the standard tree based GP approach on all
the tasks investigated here and that the programs
evolved by this approach are easier to interpret. The
investigation into the extra target registers and
program length results in heuristic guidelines for
initially setting system parameters.",
- }
Genetic Programming entries for
Mengjie Zhang
Christopher Fogelberg
Yuejin Ma
Citations