NPTEL Introduction To Soft Computing Week 6 Assignment Answers 2025
1. Which of the following is a key property of the dominance relation in Multi-Objective
Optimization Problems (MOOPs)?
a. Reflexive
b. Symmetric
c Irrellesive
d. Transitive
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2. What is the primary difference between Genetic Algorithms (GA) and Multi-Objective
Evolutionary Algorithms (MOEA) in terms of output?
a. GA produces a single solution, while MOEA produces a set of trade-off solutions.
b. GA and MOEA both produce a single optimal solution.
c. GA produces trade-off solutions, while MOEA produces a single optimal solution.
d. GA focuses on multiple objectives, while MOEA focuses on a single objective.
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3. Which of the following best describes the Pareto-optimal front in MOEA?
a. A single solution that optimizes all objectives simultaneously.
b. A set of solutions that are not dominated by any other solution.
c. A collection of random solutions that maximize diversity.
d. A population of solutions with identical fitness values.
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4. Which of the following is an a priori approach in Multi-Objective Evolutionary Algorithms
(MOEAs)?
a. Criterion selection (VEGA)
b. Lexicographic ordering
c. Pareto-based approach (NSGA-II)
d. Rank-based approach (MOGA)
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5. Which characteristic differentiates Pareto-based approaches from Non-Pareto-based
approaches in MOEAs?
a. Pareto-based approaches optimize only one objective at a time.
b. Pareto-based approaches explicitly use dominance relations to guide optimization.
c. Pareto-based approaches rank objectives based on predefined importance.
d. Pareto-based approaches require weights for combining multiple objectives.
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6. What is the primary principle behind the Single-Objective Evolutionary Algorithm (SOEA)?
a. Non-dominated sorting of solutions
b. Linear aggregation of functions
c. Criterion selection for each objective
d. Pareto dominance
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7. What is VEGA considered in the contest of Multi-Objective Evolutionary Algorithms (MOEAs)?
a. The first implementation of a MOEA
b. The most efficient Pareto-based algorithm
c. An advanced version of the NSGA-II
d. A hybrid algorithm combining NSGA and SOEA
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8. Which of the following best summarizes the disadvantages of VEGA?
a. VEGA requires extensive computational resources and fails to converge.
b. VEGA produces globally optimal solutions but struggles with diversity.
c. VEGA generates solutions with middling performance and suffers from the speciation problem.
d. VEGA is unable to handle multiple objectives and overemphasizes Pareto dominance.
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9. What is the main principle used in MOGA for solution selection?
a. Weighted sum of objectives
b. Dominance-based ranking
c. Criterion selection
d. Crowding distance
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10. What selection scheme is used in the Niched Pareto Genetic Algorithm (NPGA)?
a. Roulette wheel selection
b. Tournament selection
c. Proportional selection
d. Rank-based selection
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