Post on 04-Jan-2016
description
Genetic Algorithms (GA)
Vavilin Andrey {andy@ulsan.islab.ac.kr}
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What is GA?
GA is an heuristic search algorithm which generates solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.
Problem domain
- graph-based problems (e.g. traveling salesman problem)
- global optimization problems
- scheduling and task planning problems
- artificial intelligence tasks
- computer vision
- etc
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Function minimization example
yexyxxyxf 12cos345.09.0sin2, 22
-2
-2
2
2
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Function minimization
yexyxxyxf 12cos345.09.0sin2, 22
Gradient descent
Best point: -3.567Coordinates: 1.823, 1.549
-2
-2
2
2
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Function minimization
yexyxxyxf 12cos345.09.0sin2, 22
Random search
Iterations: 5000Best point: -3,560Coordinates: -1.899, -1.639
-2
-2
2
2
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Function minimization
yexyxxyxf 12cos345.09.0sin2, 22
Genetic algorithm
Iterations: 200Best point: -3,949Coordinates: -2, -1.960
-2
-2
2
2
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Typical genetic algorithm
Population
ParentsOffspringRecombination and
mutation
Parent selectionSurvivor selection
initialization termination
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Image processing examplesP.W.M. Tsang and Z. Yu, “Genetic algorithm for model-based matching of projected images of three-
dimensional objects”, IEE Proceedings on Vision, Image and Signal Processing, vol.150, issue 6, pp.351-359, Dec. 2003
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Image processing examplesP.W.M. Tsang and Z. Yu, “Genetic algorithm for model-based matching of projected images of three-
dimensional objects”, IEE Proceedings on Vision, Image and Signal Processing, vol.150, issue 6, pp.351-359, Dec. 2003
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Image processing examplesP.W.M. Tsang and Z. Yu, “Genetic algorithm for model-based matching of projected images of three-
dimensional objects”, IEE Proceedings on Vision, Image and Signal Processing, vol.150, issue 6, pp.351-359, Dec. 2003
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Conclusions
Advantages:
- Easy to implement
- Better than random search and faster than brute force algorithm
- Good for various classes of problems
- Easy to use with GPU-based computation
Weak points
- Specialized algorithms provide better solutions
- GA do not scale well with increasing complexity
- Bad implementation may cause algorithm converges to a local optima instead of a global one
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Image processing example
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Image processing example
Initial population
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Image processing exampleCrossover
M1
M2
Initial individuals
111111 ,,,, SySxyxMM 222222 ,,,, SySxyxMM
Individuals produced by crossover
111333 ,,,, SySxyxMM 222334 ,,,, SySxyxMM (changing position)
333337 ,,,, SySxyxMM
311335 ,,,, SySxyxMM 322336 ,,,, SySxyxMM (changing position and angle)
(changing all)
221
3
xxx
221
3
SxSxSx
221
3
yyy
221
3
SySySy
221
3
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Image processing exampleMutations
Randomly change random number of parameters in randomly select individuals. Number of individuals is 5% of population.
iiiiii SySxyxMM ,,,,
iiiiii ySxSyxMM ~,~
,~
,~,~
overwisex
truemutateXifwidthrandomx
ii ,
,,0~
overwisex
truemutateSxifScaleScalerandomxS
ii ,
,max_min,_~
overwisex
truemutateifrandom
ii ,
,360,0~
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Image processing exampleEvaluating individuals using NN
1x
Nx
Pixel values
1Nx
Nx2
…
…
Solid model
Edge model
1w
Nw
1Nw
Nw2
Probability what the tested individual is arrowhead
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Image processing exampleNN training
Training set
1x
Nx
Pixel values
1Nx
Nx2
…
…
Solid model
Edge model
1w
Nw
1Nw
Nw2
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False detection example by reference method