Adaptive problem solving method and apparatus utilizing evolutionary computation techniques
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Misc{gounares:2001:patent,
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author = "Alexander Gounares and Prakash Sikchi",
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title = "Adaptive problem solving method and apparatus
utilizing evolutionary computation techniques",
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howpublished = "U.S. Patent",
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year = "2001",
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month = "28 " # aug,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://patft.uspto.gov/netacgi/nph-Parser?Sect2=PTO1&Sect2=HITOFF&p=1&u=/netahtml/PTO/search-bool.html&r=1&f=G&l=50&d=PALL&RefSrch=yes&Query=PN/6282527",
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abstract = "A system for adaptively solving sequential problems in
a target system using evolutionary computation
techniques and in particular genetic algorithms and
modified genetic algorithms. Stimuli to a target system
such as a software system are represented as actions. A
single sequence of actions is a chromosome. Chromosomes
are generated by a goal-seeking algorithm that uses a
hint database and recursion to intelligently and
efficiently generate a robust chromosome population.
The chromosomes are applied to the target system one
action at a time and the change in properties of the
target system is measured after each action is applied.
A fitness rating is calculated for each chromosome
based on the property changes produced in the target
system by the chromosome. The fitness rating
calculation is defined so that successive generations
of chromosomes will converge upon desired
characteristics. For example, desired characteristics
for a software testing application are defect discovery
and code coverage. Chromosomes with high fitness
ratings are selected as parent chromosomes and various
techniques are used to mate the parent chromosomes to
produce children chromosomes. Children chromosomes with
high fitness ratings are entered into the chromosome
population. Defects in a target software system are
minimised by evolving ever-shorter chromosomes that
produce the same defect. Defect discovery rate, or any
other desired characteristic, is thereby maximised.",
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notes = "6,282,527 Assignee: Microsoft Corporation (Redmond,
WA)",
- }
Genetic Programming entries for
Alexander Gounares
Prakash Sikchi
Citations