an evolutionary approach to space layout planning using genetic algorithm by: hoda homayouni
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TRANSCRIPT
An Evolutionary Approach To Space Layout Planning Using
Genetic Algorithm
By: Hoda Homayouni
Introduction to Space Layout Planning
• What is Space Layout Planning?• Motivation• Challenges:
– Solving ill defined problems– Addressing qualitative constraints– Having creativity– Compatibility with architects
Introduction to Genetic Algorithm
Computer Algorithm that resides on principles of genetic and evolution.
4
Why Genetic Algorithm?
• Hill climbing
locallocal
globalglobal
Why Genetic Algorithm?
• Multi-climbers
Why Genetic Algorithm?
• Genetic algorithm
I am not at the top.I am not at the top.My high is better!My high is better!
I am at the I am at the toptop
Height is ...Height is ...
I will continueI will continue
Why Genetic Algorithm?
• Genetic algorithm
few microseconds after
Encoding Chromosomes
• The chromosome should in some way contain information about solution which it represents
Crossover
• Crossover selects genes from parent chromosomes and creates a new offspring
Mutation
• This is to prevent falling all solutions in population into a local optimum of solved problem
Fitness Function
• Fitness function is evaluation function,that determines what solutions are better than others.
• Fitness is computed for each individual.
• Fitness function is application depended.
Algorithmic Phases
Initialize the populationInitialize the population
Select individuals for the mating poolSelect individuals for the mating pool
Perform crossoverPerform crossover
Insert offspring into the populationInsert offspring into the population
The EndThe End
Perform mutationPerform mutation
yesyes
nono
Stop?Stop?
Genetic Engineering Approach
M1
M3
M2
M4
• An object can be described by the location of units and can be ‘grown’ by locating a required number of such units, one at a time in sequence.
Genetic Engineering
Evolving Complex Design Genes Using a Hierarchical Growth
Approach
HOUSE
ZONE1 ZONE2 ZONEz• • • • • • • • • •
ROOM1 ROOM2 ROOMm• • •
S_UNIT1 S_UNIT2 S_UNITs• • •
Generating Units
W1
S2
N1 N2
E1
S1
W1
S2
N1
W2
N2
E1
E2
S1
W1
S1
N1
E1
P1(p) = (W1,N1,E1,S1)P2(g) = (P1,P1,E1|W1) P2(p) = (W1,N1,N2,E1,S1,S2)
P3(g) = (P2,P1,N2|S1) P3(p) = (W1,N1,W2,N2,E1,E2,S1,S2)
Crossover at Room Level
R1 = (N1|S1, E1|W1, E1 |W1, S1|N1, S1|N1)
1 2 3R2 = (E1 |W1, N1 |S1, W 1|E1, N2|S1, W 3|E1)
1 2 34 4
N1
W1
W2S1
W3
N2 N3
E1
E2
E3
S2S3S1
W1
W2
N1
S3
E4
E1
N2
S2
N3
W4E2
E3W3
S4
N4
R1 = (N1|S1, E1|W1, E1|W1, S1|N1, S1 |N1) R2 = (E1 |W1, N1|S1, W1|E1, N2 |S1, W3 |E1)
R3 = (N1|S1, E1|W1, W1|E1, N2 |S1, W3 |E1) R4 = (E1 |W1 , N1|S1, E1|W1, S1|N1, S1 |N1)
2
Crossover at Room Level
R1 = (N1|S1, E1|W1, E1 |W1, S1|N1, S1|N1)
1 2 3R2 = (E1 |W1 , N1 |S1, W 1|E1 , N2 |S1, W 3|E1)
1 2 34 4
N1
W1
W2S1
W3
N2 N3
E1
E2
E3
S2S3S1
W1
W2
N1
S3
E4
E1
N2
S2
N3
W4E2
E3W3
S4
N4
R1 = (N1|S1, E1|W1, E1|W1, S1|N1, S1 |N1) R2 = (E1 |W1, N1|S1, W1|E1, N2 |S1, W3 |E1)
R5 = (N1|S1, E1|W1, E1|W1, S1|N1, W3 |E1) R6 = (E1 |W1, N1|S1, W1|E1, N2 |S1, S1 |N1)
4
Crossover at Site LevelLiving Room EntranceDining Room
D1 E2D2L1 L2
E1
Z1 = ((L1, D2, E4|W2), E1, N5|S3)
1 2 3 4
E1
D2L1
Z2 = ((L2, D1, E3|W2), E2, S2|N3)
1 2 3 4
L2 D1
E2
Z5 = ((L1, D2, E3|W2), E2, S2|N3) Z6 = ((L2, D1, E4|W2), E1, N5|S3)
SITE 2 - CROSSOVER
D2
E2
L1 E1L2
D1
Initial Living Zone Population
Evolved Population
Initial House population
Evolved Population
Discussion
• More Fitness Functions
• Architects Role?
References
• Rosenman, M.A. (1997). The Generation of form using evolutionary approach”. Evolutionary algorithms in Engineering Applications. Springer, 1997.
• Rosenman, M.A. and Gero, J.S. (1999) Evolutionary designs by generating useful complex gene structures. Evolutionary Design by Computers, Morgan Kaufmann, San Francisco, pp.345-364.
• http://galeb.etf.bg.ac.yu/~vm/GenAlgo.ppt