About My Research

1. Evolutionary Computation
And Applications
Deepak Dawar
04/25/15 DD - North Dakota State University 1

2. Problems Galore!
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3. The Traveling Salesman
• NP Complete
• It is likely that the worst case
running time for any algorithm
for the TSP increases
exponentially with the number
of cities.
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4. Evolutionary Algorithms
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5. Classes of Search Methods
Search Method
Calculus Based
Method
Guided Random Search
Method
Enumerative
Method
BFSDFS Dynamic
Programming
Tabu
Search
Hill
Climbing
Simulated
Anealing
Evolutionary
Algorithms
Genetic
Algorithms
Fibonacci Sort
Swarm
Intelligence
Particle
Swarm
Ant
Colony
Differential
Evolution
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6. Evolutionary Algorithms
• Emulate evolutionary processes.
• Operate on population of individuals.
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7. List of Parts for Real Parameter
Optimization with EA
• A function to optimize
• A domain in which to optimize
• An Evolutionary Algorithm
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8. The Function and The Domain
Function:
We want to find the minimum value on
the landscape.
8
Global
Minimum
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9. The Algorithm-Differential Evolution
• Real Parameter Optimizer
• Utilizes a pre-specified number of random solutions
• Continuously improves them through a series of mutations and re-
combinations
• Usually, the number of pre-specified solutions i.e. the population size
does not change during the lifetime of the algorithm
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10. Differential Evolution
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11. Important Nomenclature
• D = Dimensionality of the problem
• NP = No. of solution vectors in a
population
• G = No. of generations the population has to go
through
• F = Scaling Factor
• Cr = Crossover rate
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12. Initialization
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13. Initial Population
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14. Mutation
• Mutation is a change in the gene characteristics of a chromosome.
• Applied to evolutionary computation it means a change in the
parameters of the vector through a perturbation with a random
element.
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15. Mutation
• A parent vector from the current generation is called target vector
• The mutant vector obtained through differential perturbation is
called donor vector
• The offspring obtained through recombination of target and donor is
called trial vector
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16. Mutation
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17. Mutation
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18. Crossover
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19. Create Trial Vector
• Suppose:
rand =0.945, Cr =0.9
• If rand < Cr, pick the
parameter from the
donor
• Else from the parent
• Do this for all 10
parameters
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20. Selection- Choose between Parent and
Child/Trial Vector
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21. Control Parameters
• NP, F and Cr are collectively termed as control parameters of DE
• The performance of DE is very sensitive to the values of control
parameters
• Every problem/function may respond differently to different sets of
control parameters.
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22. Benchmark Functions-
IEEE CEC 2013 Test Suite
• State of the art benchmark functions used in CEC Real Parameter
Optimization Competitions and Conferences
• Emulate the properties of real world large scale optimization
problems
• IEEE CEC Test suites have constantly evolved over time with the
advances in the field of Large Scale Global Optimization
• All the problems listed in the Test Suite are minimization problems
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23. Rotated Ackley’s Function
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24. Scaling Factor
Scales the difference of solutions and controls the step size
Many Authors have reported that its effective values lie between [0.4, 1]
though it may ultimately also depend upon the problem landscape
Contradicting results have been reported for what should be good values
for the scaling factor
So what do we do?
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25. Altering the Scaling Vector
Deterministic - the parameters are altered based on some user defined rules
Adaptive - the parameters are allowed to adapt based on some feedback from
the algorithm
Evolutionary - the parameters are encoded into the solution itself and they
evolve as a part of the general population
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26. In Our First Paper
• We primarily focus our attention towards deterministic parameter
control methods and the control parameter, scale factor (F), in
particular.
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27. Dither
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28. DETVSF - DE with Time Varying Scale Factor
 Linear Reduction
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29. DEDASF - DE with Dither and Annealed Scale Factor
 Non Linear Reduction
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30. Birds Eye View
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31. What do we achieve?
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32. Result 1 - FriedMan’s Test - Ranks
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33. Result 2 - Hocheberg’s Post Hoc
Procedure: significance level 0.1
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34. Second Paper - Objective
• To detect, classify
and count the
type of moving
vehicles
34Source: http://www.123rf.com/stock-photo/highway.html04/25/15 DD - North Dakota State University

35. Vehicle Recognition Steps
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36. How to Identify Axles?
• Axles are circular
• Shape recognition algorithm must be employed
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37. Hough Transform
• Is a feature extraction technique used in image analysis
• Can be used to detect lines, circles, and ellipses or any shape
represented by a set of parameters
• We focus on circle detection
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38. Recognition Example (1)
• Original Image
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39. Recognition Example (1)
• dp (accumulator resolution)=1.25
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40. Recognition Example (2)
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41. Recognition Example (3)
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42. 04/25/15 DD - North Dakota State University 42
DEMO

43. Paper 3 - Object Tracking
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44. Stages In Object Tracking
• Object Initialization
• Appearance model Initialization
• Search – Object Localization
• Appearance model Updation
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45. Modeling The Target
• Represent the target as
• Vector
• Matrix
• Histogram
• Wavelets
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46. Modeling – A Difficult Task
• Scene changes
• Illumination
• Background
• Occlusion
• Deformation
• Noise
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47. Univariate Gaussian
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48. Multivariate Gaussian
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49. Gaussian Mixture
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50. Segmentation using GMM
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51. Demo
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Thank You