Computational Intelligence - August 2014 - 79
NumbeR of
TeRRaces
aRea
obsidiaN
shell
ceRamic
liThic
maNo & meTaTe
eliTes
ResideNTial
disTaNce fRom
Road
disTaNce fRom
mp
disTaNce fRom
miNoR GaTes
disTaNce fRom
majoR GaTes
aRea
obsidiaN
shell
ceRamic
liThic
maNo & meTaTe
eliTes
ResideNTial
disTaNce fRom
Road
disTaNce fRom
mp
disTaNce fRom
miNoR GaTes
disTaNce fRom
majoR GaTes
Fitness Value (Average Regional Scores)
NumbeR of
TeRRaces
situational, normative, and
Table 16 The craft weights are used to reward each proposed region based upon the extent to which
topographical) were used
it satisfies the rules associated with each functional category. The weights reflect the relative
importance of each category in the assessment of the proposed region.
in the Cultural Algorithm
in order to influence the
Rules
sTRucTuRe
conTenT
conTexT
population of city planner
models. Domain knowledge has a set of rules for
Rule's
each functional class (elite
Type
residential, non-elite residential, and crafts). Situational knowledge stores the
WeighT %
0.05
0.05
0.10 0.05 0.05 0.10
0.10
0.05
0.10
0.05
0.10
0.10
0.10
best 20% of the population
plans in the belief space.
Table 17 The non-elite residential weights are used to reward each proposed region based upon the
Normative knowledge
extent to which it satisfies the rules associated with each functional class. The weights reflect the
adjusts the minimum and
relative importance of each category in the assessment of the proposed region.
maximum number of
Rules
sTRucTuRe
conTenT
conTexT
regions for each agent in
each generation. Topographical knowledge stores
Rule's
the centroids of the best
Type
20% of the planner population. The centroids represent the possible center
WeighT %
0.10
0.10
0.05 0.05 0.05 0.05
0.10
0.05
0.20
0.05
0.10
0.05
0.05
locations for the regions
proposed by the plan.
conducted separately, and the
Here, the evolutionary plan1
results combined at the end.
ning process was subdivided into
For each functional class, 50
three parts, one for each of the
0.9
runs were conducted with a
three functional classes: elite resi0.8
total of 150 runs overall. The
dential; non-elite residential; and
0.7
number of agents chosen for
crafts. This substantially reduced
0.6
each run was 20, and the numthe complexity of the plan evober of generations was set to 60.
lution process since each func0.5
The rationale for setting the
tional class plan was evolved sep0.4
number of generations to 60
arately, and the three results
0.3
was that in all of the trial runs a
combined at the end. This was
0.2
population of size 20 found no
done for two main reasons. First,
Max (Best)
Ave
new improvement to the best
it was assumed that the domi0.1
Min
plan after 60 generations. Since
nant factors in the site plan for
0
the computational time for
this early phase were the basic
0
10
20
30
40
50
60
Generation
each generation was significant,
attractors for the site: the road
restricting the run length alnetwork and the central plaza. In
FiguRe 16 The learning curve results for the best non-elite resilowed more experiments to be
other words, initially terrace dential run out of 50 runs where each run takes 60 generations.
conducted. The best plan fitness
placement reflected their loca- The fitness value for this run is 87%.
for the craft category was in
tion relative to these attractors
generation 17 with a fitness of 93%. The
multi-dimensional scaling, each of the
rather than other terraces. In future
best plan fitness for the elite residential
three functional classes separated out quite
phases, when certain portions of the site
class was in generation 19 with a fitness
distinctly from each other when plotted
are already occupied, the relative location
of 83%. The best fitness for the non-elite
in terms of the three general dimensions
of a new terrace can be influenced by
residential class was in generation 20
produced in the analysis.This suggests that
those terraces already there. However, it
with a fitness of 87%. The best possible
the terraces are more likely to be located
was clear from the decision tree analysis
fitness for each functional category is
near similar types of terraces rather than
for phase Ia that there are more desirable
100%, which means that the model pronear terraces of different type.
terraces in this phase than there are peoduced by an agent with this fitness value
Therefore, the planning process for
ple to settle them so this is presumed to
is a perfect fit with the characteristics in
each of the three functional classes was
be much less of a factor. Second, in the
august 2014 | IEEE ComputatIonal IntEllIgEnCE magazInE
79
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