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Fig. 2.1 Illustrative diagram of “Klondike spaces” ( left ,afterBell 1999 ) and, characterisation of
archetypical search spaces in Evolutionary Computing ( right ,afterLuke 2009 )
the creative search of a conceptual space (Fig. 2.1 , left): (i) rarity: viable solutions
are sparsely distributed in a vast space of non-viable possibilities; (ii) isolation :
places of high creative value in the conceptual space are widely separated and dis-
connected, making them difficult to find; (iii) oasis : existing solutions offer an oasis
that is hard to leave, even though better solutions might exist elsewhere; (iv) plateau :
many parts of the conceptual space are similar, giving no clues as to how to proceed
to areas of greater creative reward.
This classification is similar to archetypical search and optimisation problems
encountered in EC (Fig. 2.1 , right), where algorithms search for optima in what are
often difficult phenotypic spaces (Luke 2009 ). For example, “rarity” corresponds to
“Needle in a haystack”, “oasis” to “Deceptive”. Noisy landscapes are particularly
problematic, where evolutionary methods may do no better than random search.
Knowing as much as possible about the structure of the space you are searching
is immensely important, as it allows you to strategically search using the most ef-
ficient methods. Additionally, being able to restructure the space can make it more
intuitive for creative exploration. Hence the design of any creative system should
take the structural design of the creative space very seriously. It is also important
to emphasise that the search process is an explorative one. For most creative sys-
tems, this search space is Va s t (McCormack 2008b ), and there may be many iso-
lated “Klondike spaces” of rich creative reward. The challenge is to efficiently and
effectively find and explore them.
2.1.1 Spaces of Possibility
We should make further distinctions about creative spaces and spaces of possibility.
As I have previously discussed (McCormack 2008b ), in many domains there are
large and crucial differences between the possible and actual. For example, consider
a digital image defined by executing an arbitrary Lisp expression over some do-
main (x, y) , where x and y are the co-ordinates of a rectangular grid of pixels that
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