Journal article
Adaptive enlargement of state spaces in evolutionary designing
AI EDAM, v 14(1), pp 31-38
Jan 2000
Abstract
In designing a state space of possible designs is
implied by the representation used and the computational
processes that operate on that representation. GAs are
a means of effectively searching that state space which
is defined by the length of the genotype's bit string.
Of particular interest in design computing are processes
that enlarge that state space to change the set of possible
designs. This paper presents one such process based on
the generalization of the genetic crossover operation.
A crossover operation of genetic algorithms is reinterpreted
as a random sampling of interpolating phenotypes, produced
by a particular case of phenotypic interpolation. Its generalization
is constructed by using a more general version of interpolation
and/or by adding extrapolation to interpolation. This generalized
crossover has a potential to move the current population
outside of the original state space. An adaptive strategy
for state space enlargement, which is based on this generalization,
is designed. This strategy can be used for computational
support of creative designing. An example is given.
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Details
- Title
- Adaptive enlargement of state spaces in evolutionary designing
- Creators
- JOHN S. Gero - The University of SydneyVLADIMIR Kazakov - The University of Sydney
- Publication Details
- AI EDAM, v 14(1), pp 31-38
- Publisher
- Cambridge University Press
- Number of pages
- 8
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000086498000004
- Scopus ID
- 2-s2.0-0033742498
- Other Identifier
- 991022156310704721