EvoArch: An evolutionary algorithm for architectural layout design
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Wong2009649,
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author = "Samuel S. Y. Wong and Keith C. C. Chan",
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title = "{EvoArch:} An evolutionary algorithm for architectural
layout design",
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journal = "Computer-Aided Design",
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volume = "41",
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number = "9",
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pages = "649--667",
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year = "2009",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Architectural
space topology, Evolutionary algorithm, Crossover,
Graph algorithm, Mutation",
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ISSN = "0010-4485",
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URL = "https://research.polyu.edu.hk/en/publications/evoarch-an-evolutionary-algorithm-for-architectural-layout-design",
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broken = "http://www.sciencedirect.com/science/article/B6TYR-4W6XW17-2/2/8b37ad1171b7fd66aaeb17f58baf7ee0",
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DOI = "doi:10.1016/j.cad.2009.04.005",
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size = "19 pages",
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abstract = "The architectural layout design problem, which is
concerned with the finding of the best adjacencies
between functional spaces among many possible ones
under given constraints, can be formulated as a
combinatorial optimisation problem and can be solved
with an Evolutionary Algorithm (EA). We present
functional spaces and their adjacencies in form of
graphs and propose an EA called EvoArch that works with
a graph-encoding scheme. EvoArch encodes topological
configuration in the adjacency matrices of the graphs
that they represent and its reproduction operators
operate on these adjacency matrices. In order to
explore the large search space of graph topologies,
these reproduction operators are designed to be
unbiased so that all nodes in a graph have equal
chances of being selected to be swapped or mutated. To
evaluate the fitness of a graph, EvoArch makes use of a
fitness function that takes into consideration
preferences for adjacencies between different
functional spaces, budget and other design constraints.
By means of different experiments, we show that EvoArch
can be a very useful tool for architectural layout
design tasks.",
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notes = "Department of Computing, The Hong Kong Polytechnic
University, China",
- }
Genetic Programming entries for
Samuel S Y Wong
Keith C C Chan
Citations