CS-702-A Computational Intelligence - Nov 2023 Question Paper

Under each question, How to answer lists the points an answer should cover, outlined by CampusPrep from the syllabus. Check the details against your textbook.

1. 7 Marks
   aExplain all the components of Computational with help of example.
   How to answer

   • Define Computational Intelligence and its scope
   • Explain components: neural networks, fuzzy systems, evolutionary computation etc. with roles
   • Give example for each component
   • Illustrate interaction of components
2. 7 Marks
   bExplain the working of Parametric Model and explain its application in statistics.
   How to answer

   • Define Parametric Model
   • Explain working steps and parameters
   • Explain application in statistics
   • Give illustrative example
3. 7 Marks
   aDesign Feed Forward Neural Network to Solve Singular Boundary Value Problems.
   How to answer

   • State singular boundary value problem formulation
   • Describe feed-forward network architecture for solution
   • Explain trial solution and loss function construction
   • Outline training procedure
4. 7 Marks
   bLet fuzzy sets A and B be given as: $A = 0.5/3 + 1/5 + 0.6/7 + 0.8/8$ $B = 1/3 + 0.5/5 + 0.1/7 + 1/8$ where the universe of discourse being $X = \{3, 5, 7, 8\}$. Now obtain the following:

   1. i)A+B, the Algebraic Sum
   2. ii)A.B, the Algebraic Product
   3. iii)S(A,B) the subsethood measure
   4. iv)E(A,B) the equality measure
   How to answer

   • Recall formulas for algebraic sum and product
   • Compute $A+B$ and $A.B$ element-wise
   • Compute subsethood measure $S(A,B)$
   • Compute equality measure $E(A,B)$
5. 7 Marks
   aDefine alpha cut, strong alpha cut sets and level sets of a give fuzzy set with help of your data set.
   How to answer

   • Define alpha cut, strong alpha cut and level set
   • Give mathematical definitions with notation
   • Illustrate with own data set example
   • Show resulting crisp sets for different alpha
6. 7 Marks
   bExplain the concept of Fuzzy relations and their composition with help of examples.
   How to answer

   • Define fuzzy relation
   • Explain types and representation
   • Explain composition: max-min, max-product etc.
   • Give numerical examples
7. 7 Marks
   aExplain the procedures of genetic algorithms and what are the different genetic representations.
   How to answer

   • Outline GA cycle: initialization, selection, crossover, mutation
   • Explain fitness evaluation and termination
   • Describe binary, real, permutation and other representations
   • Give example of each
8. 7 Marks
   bA budget airline company operates 3 plains and employs 5 cabin crews. Only one crew can operate on any plain on a single day, and each crew cannot work for more than two days in a row. The company uses all planes every day. A Genetic Algorithm is used to work out the best combination of crews on any particular day.

   1. i)Suggest what chromosome could represent an individual in this algorithm?
   2. ii)Suggest what could be the alphabet of this algorithm? What is its size?
   How to answer

   • Model daily crew assignment under constraints
   • Propose chromosome encoding for 3 planes
   • Define alphabet as crew identifiers
   • Determine alphabet size
9. 7 Marks
   aExplain different Encoding Techniques available for Genetic Algorithms with help of examples.
   How to answer

   • List binary, octal, hexadecimal, permutation, value encodings
   • Explain each with structure
   • Give example chromosome for each
   • Mention suitability for GA operators
10. 7 Marks
    bExplain the difference between fuzzy set and rough fuzzy sets with the help of examples.
    How to answer

    • Define fuzzy set with membership function
    • Define rough set and rough fuzzy set approximations
    • Compare using tabular differences
    • Give examples of each
11. 7 Marks
    aExplain the rough set-based decision tree with help of example.
    How to answer

    • Define rough sets: indiscernibility, approximations
    • Explain integration with decision tree splitting
    • Give example data set and tree construction
    • Mention advantages
12. 7 Marks
    bExplain Hidden Markov Models with its application and with help of suitable example.
    How to answer

    • Define Hidden Markov Model components
    • Explain states, observations, transition probabilities
    • Describe applications e.g. speech recognition
    • Give suitable example calculation
13. 7 Marks
    aWhy the Ant colony optimization, Particle Swarm optimization and other techniques are used for feature selection?
    How to answer

    • Explain need for dimensionality reduction
    • Explain role of ACO, PSO in search for optimal subset
    • Discuss handling of large search space
    • Mention improved classifier performance
14. 7 Marks
    bExplain the working of Particle Swarm Optimization (PSO Algorithm) with suitable help of example.
    How to answer

    • Define PSO particles, velocity, pbest, gbest
    • Explain velocity and position update formulas
    • Illustrate iterations with example
    • Mention convergence criteria
15. 14 Marks
    Write a short note on any two.

    1. i)Defuzzification
    2. ii)Genetic Operators
    3. iii)Bee Colony Optimization
    4. iv)Fuzzy Rules
    How to answer

    • Select any two topics from list
    • Define each with key terms and steps
    • Give example/diagram where applicable
    • Mention applications