The Hierarchical Fair Competition Framework for Sustainable Evolutionary Algorithms
Created by W.Langdon from
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- @Article{hu:2005:EC,
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author = "Jianjun Hu and Erik Goodman and Kisung Seo and
Zhun Fan and Rondal Rosenberg",
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title = "The Hierarchical Fair Competition Framework for
Sustainable Evolutionary Algorithms",
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journal = "Evolutionary Computation",
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year = "2005",
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volume = "13",
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number = "2",
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pages = "241--277",
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month = "Summer",
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keywords = "genetic algorithms, genetic programming, sustainable
evolutionary algorithms, building blocks, premature
convergence, diversity, fair competition, hierarchical
problem solving",
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ISSN = "1063-6560",
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publisher = "MIT Press",
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broken = "http://www.ingentaconnect.com/content/mitpress/evco/2005/00000013/00000002/art00005",
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DOI = "doi:10.1162/1063656054088530",
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size = "37 pages",
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abstract = "Many current Evolutionary Algorithms (EAs) suffer from
a tendency to converge prematurely or stagnate without
progress for complex problems. This may be due to the
loss of or failure to discover certain valuable genetic
material or the loss of the capability to discover new
genetic material before convergence has limited the
algorithm's ability to search widely. In this paper,
the Hierarchical Fair Competition (HFC) model,
including several variants, is proposed as a generic
framework for sustainable evolutionary search by
transforming the convergent nature of the current EA
framework into a non-convergent search process. That
is, the structure of HFC does not allow the convergence
of the population to the vicinity of any set of optimal
or locally optimal solutions. The sustainable search
capability of HFC is achieved by ensuring a continuous
supply and the incorporation of genetic material in a
hierarchical manner, and by culturing and maintaining,
but continually renewing, populations of individuals of
intermediate fitness levels. HFC employs an
assembly-line structure in which subpopulations are
hierarchically organised into different fitness levels,
reducing the selection pressure within each
subpopulation while maintaining the global selection
pressure to help ensure the exploitation of the good
genetic material found. Three EAs based on the HFC
principle are tested - two on the even-10-parity
genetic programming benchmark problem and a real-world
analog circuit synthesis problem, and another on the
HIFF genetic algorithm (GA) benchmark problem. The
significant gain in robustness, scalability and
efficiency by HFC, with little additional computing
effort, and its tolerance of small population sizes,
demonstrates its effectiveness on these problems and
shows promise of its potential for improving other
existing EAs for difficult problems. A paradigm shift
from that of most EAs is proposed: rather than trying
to escape from local optima or delay convergence at a
local optimum, HFC allows the emergence of new optima
continually in a bottom-up manner, maintaining low
local selection pressure at all fitness levels, while
fostering exploitation of high-fitness individuals
through promotion to higher levels.",
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notes = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=4&tid=25",
- }
Genetic Programming entries for
Jianjun Hu
Erik Goodman
Kisung Seo
Zhun Fan
Rondal Rosenberg
Citations