A Strongly Feasible Evolution Program for non-linear optimization of Network Flows
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @PhdThesis{ilich:2000:thesis,
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author = "Nesa Ilich",
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title = "A Strongly Feasible Evolution Program for non-linear
optimization of Network Flows",
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school = "Department of Civil and Geological Sciences,
University of Manitoba",
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year = "2000",
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address = "Winnipeg, Canada",
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month = oct,
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email = "NIlich@mail.com",
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keywords = "genetic algorithms, genetic programming, Evolution
Programs, Network Flows, Non-Linear Constraints",
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URL = "http://mspace.lib.umanitoba.ca/bitstream/1993/1759/1/NQ57510.pdf",
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size = "163 pages",
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abstract = "This thesis describes the main features of a Strongly
Feasible Evolution Program (SFEP) for solving network
flow programs that can be non-linear both in the
constraints and in the objective function. The approach
is a hybrid of a network flow algorithm and an
evolution program. Network flow theory is used to help
conduct the search exclusively within the feasible
region, while progress towards optimal points in the
search space is achieved using evolution programming
mechanisms such as recombination and mutation. The
solution procedure is based on a recombination operator
in which all parents in a small mating pool have equal
chance of contributing their genetic material to an
offspring. When an offspring is created with better
fitness value than that of the worst parent, the worst
parent is discarded from the mating pool while the
offspring is placed in it. The main contributions are
in the massive parallel initialization procedure which
creates only feasible solutions with simple heuristic
rules that increase chances of creating solutions with
good fitness values for the initial mating pool, and
the gene therapy procedure which fixes {"}defective
genes{"} ensuring that the offspring resulting from
recombination is always feasible. Both procedures use
the properties of network flows. Tests were conducted
on a number of previously published transportation
problems with 49 and 100 decision variables, and on two
problems involving water resources networks with
complex non-linear constraints with up to 1500
variables. Convergence to equal or better solutions was
achieved with often less than one tenth of the previous
computational efforts.",
-
notes = "Bighorn/Brazeau hydro power. Brantas river basin in
east Java.
",
- }
Genetic Programming entries for
Nesa Ilich
Citations