Online ordering is currently unavailable due to technical issues. We apologise for any delays responding to customers while we resolve this. For further updates please visit our website: https://www.cambridge.org/news-and-insights/technical-incident
We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure coreplatform@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
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.
Evolutionary and Adaptive strategies (ES &
AS) for diverse multilevel search across a preliminary,
whole-system design hierarchy defined by discrete and continuous
variable parameters are described. Such strategies provide
high-level decision support when integrated with preliminary
design software describing the major elements of an engineering
system. Initial work involving a Structured Genetic Algorithm
(stGA) with appropriate mutation regimes to encourage search
diversity is described and preliminary results are presented.
The shortcomings of the stGA approach are identified and
alternative strategies are introduced. A dual agent strategy
(GAANT) involving elements of an ant colony search and
an evolutionary search concurrently manipulating the discrete
and continuous variable parameter sets is presented. Appropriate
communication between the two search agents results in
a more efficient search across the hierarchy than that
achieved by the stGA, while also simplifying the chromosomal
representation. This simplification allows the further
development of the preliminary design hierarchy in terms
of complexity. The technique therefore represents a significant
contribution to configuration design where multilevel,
mixed discrete/continuous parameter design problems can
be prevalent.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.