Soft Computing (KCA032) - AKTU Question Paper 2024-25
MCA · Semester 4 · Free PDF Download
This is the official AKTU Soft Computing Previous Year Question Paper for MCA Semester 4, academic session 2024-25. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.
Course:MCA
Semester:Semester 4
Session:2024-25
University:AKTU / UPTU
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Questions Asked in 2024-25
Soft Computing (KCA032) — extracted question text · 100 marks · 3 Hours
Section AAttempt all questions in brief. 2 x 10 = 20
- aWhat are the key differences between soft computing and hard computing?
- bDefine an artificial neuron and describe its basic structure
- cWhat is the role of activation function in a neural network?
- dExplain the concept of fu zzification and its purpose
- eList different types of learning in neural networks
- fWhat are the key operation s in a genetic algorithm?
- gDefine membership function in fuzzy logic
- hWhat is crossover in genetic algorithms and why is it importan t? 4 K5 i. Explain the term ‘swarm intelligence’. 5 K3 j. What are hybrid soft computi ng techniques? Give an example
Section BAttempt any three of the following: 10 x 3 = 30
- aCritically evaluate how the choice of activation function a ffects the learning capability, convergence, and performance of a neural n etwork in real-world applications suc h as image recognition or languag e processing. Justify your reasoning with suitable examples
- bExplain the architecture and training process of multilayer perceptron
- cVarious Fuzzy set operations e xtend classical set theory to handle uncertainty and vagueness. Critical ly analyze how these operati ons enable better modeling of real-wo rld problems compared to crisp logic. Choose a real-life scenario (e.g., air conditioner control, risk assessment, or medical diagnosis) and demonstrate how fuzzy set operations enhance decision-making in that context
- dExplain the structure and wo rking of a Kohonen Self-Organizing Map
- eExplain the concepts of Genetic Algorithm. Analyze and evalu ate how each step of a GA—selection, c rossover, and mutation—contribute s to finding optimal or near-optimal s olutions. Also, discuss potent ial challenges in applying GAs effectively
Section CAttempt any one part of the following: 10 x 1 = 10
- aExplain supervised, unsupervised, and reinforcement learning w i t h suitable examples
- bCritically analyze how soft co mputing differs from hard comp uting in dealing with real-world comple xity. Choose a practical applicat ion domain (such as medical diagnosis, autonomous vehicles, or fina ncial prediction) and argue why soft c omputing is better suited than traditional hard computing approaches
- aCompare and contrast Perceptr on and Backpropagation networks. Also, Design a perceptron that impleme nts the logical AND function. P rovide the weight updates for each training step using a learning rate of 1
- bExplain the architecture and features of a Hopfield Network. 2 K 4
- aExplain fuzzy inference system with a diagram. Describe its components. Also, Given fuzzy set A = {(1, 0.3), (2, 0.5), (3, 0.7), (4, 1)}, compute the α-cut for α = 0.6 and the complement of A
- bDifferentiate between Mamdani and Sugeno inference systems
- aExplain the process of encoding, crossover, and mutation in GA with examples. Also, Apply single-point crossover and mutation on: Parent 1: 11010101 Parent 2: 10101011 Crossover after 4th bit; fli p 3rd bit of offspring
- bCompare and contrast Genetic A lgorithms (GAs), Genetic Programming (GP), and Evolutionary Strategie s (ES) in the context of solvin g real- world optimization problems. Choos e one real-world problem (e.g ., route optimization, financial forecasting, robotic control, etc.) and justify which of the three would be the most appropriate approach and why
- aDescribe the neuro-fuzzy hybrid system with a suitable application
- bSwarm Intelligence algorithms such as Particle Swarm Optimiz ation (PSO) and Ant Colony Optimization (ACO) are inspired by collect ive behavior in nature. Critically e valuate how these algorithms mi mic real- world swarm behavior to solve optimization problems. Choose a specific application (e.g., network routi ng, task scheduling, or vehicle routing) and analyze the advantages and limitations of using swarm intel ligence for that problem
Question text is extracted from the official AKTU question paper PDF above. Check the original PDF for equations, diagrams and complete wording. Extracted: 2026-08-23.
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