ML/AI Flashcards
What is the primary inspiration for the GWO algorithm?
a) Bird migration patterns
b) Social behavior of wolves
c) Genetic structures
d) Firefly communication
Answer: b
In GWO, what does the prey represent in the optimization process?
a) The worst solution
b) The optimal solution
c) The alpha wolf
d) The average solution
Answer: b
What is the significance of the social hierarchy in a wolf pack for the GWO algorithm?
a) It determines the pack’s hunting strategy
b) It establishes dominance for all wolves
c) It guides the optimization process
d) It influences the color of wolves
Answer: c
In Grey Wolf Optimization the Alpha wolf
a) Ranks first and represents the best solution
b) Ranks second and assists the alpha by exploring the solution space
c) Ranks third and independently explores the solution space
d) None of the above
Answer: a
In Grey Wolf Optimization the Beta wolf
a) Ranks first and represents the best solution
b) Ranks second and assists the alpha by exploring the solution space
c) Ranks third and independently explores the solution space
d) None of the above
Answer: b
In Grey Wolf Optimization the Delta wolf
a) Ranks first and represents the best solution
b) Ranks second and assists the alpha by exploring the solution space
c) Ranks third and independently explores the solution space
d) None of the above
Answer: c
In Grey Wolf Optimization the Omega wolf
a) Ranks first and represents the best solution
b) Ranks second and assists the alpha by exploring the solution space
c) Ranks third and independently explores the solution space
d) None of the above
Answer: d
What is the time complexity of the GWO algorithm approximately expressed as?
a) O(n^2)
b) O(k+n)
c) O(k*n)
d) O(log n)
Answer: O(k*n)
How is the position of wolves updated in GWO?
a) Only based on alpha wolf
b) Average of the three best wolves
c) Randomly without considering other wolves
d) According to the prey’s position
Answer: b
What is the main application for GWO in machine learning?
a) Image recognition
b) Hyperparameter tuning
c) Natural language processing
d) Clustering
Answer: b
How is GWO used in hyperparameter tuning?
a) By optimizing learning rate, number of trees, and maximum depth
b) By selecting the fastest algorithm
c) By adjusting image resolution
d) By tuning neural network weights
Answer: a
What is a more feasible representation for feature selection in GWO?
a) Numeric values for each feature
b) A binary vector representing included features
c) Randomly selected features
d) A matrix of feature correlations
Answer: b
What does GWO offer as a cost-effective solution for feature selection?
a) Exponential complexity
b) Linear complexity
c) Binary complexity
d) Hierarchical complexity
Answer: b
In GWO, why is a small random shift added to the position update of wolves?
a) To confuse the prey
b) To avoid getting stuck in local optima
c) To increase dominance
d) To speed up convergence
Answer: b
What does the gathering of omega wolves around the best three wolves indicate in GWO?
a) The end of the optimization process
b) Increased randomness
c) Failure of the algorithm
d) Lack of hierarchy
Answer: a
What type of algorithm is SSA?
a) Genetic Algorithm
b) Population-based Algorithm
c) Neural Network
d) Decision Tree
Answer: b
What does SSA mimic in its design?
a) Birds
b) Fish
c) Salp Swarms
d) Bees
Answer: c
What is the natural behavior of salp chains in the depths of the oceans?
a) They move by flying in the air
b) They move by pushing water to find food
c) They move by crawling on the ocean floor
d) They move by floating on the water surface
Answer: b
How long can the chains of salp swarms be?
a) Up to 2 feet
b) Up to 5 feet
c) Up to 10 feet
d) Up to 15 feet
Answer: d
What is the main reason for the behavior of salp chains?
a) To avoid predators
b) To achieve better movement for finding food
c) To migrate to different oceans
d) To reproduce more efficiently
Answer: b
How is the leader-follower structure mathematically described in the salp swarm?
a) The head salp is the follower, and others are leaders
b) The head salp is the leader, and others are followers
c) All salps in the chain are leaders
d) All salps in the chain are followers
Answer: b
What is the main characteristic of the Salp Swarm Algorithm (SSA)?
a) It is a deterministic algorithm
b) It is a random (stochastic) algorithm
c) It is a rule-based algorithm
d) It is a supervised learning algorithm
Answer: b
In the Salp Swarm Algorithm, what does the exploration stage focus on?
a) Finding better solutions
b) Exploiting local data to improve the current solution
c) Balancing between exploration and exploitation
d) Updating the position of the leader salp
Answer: a
What is the purpose of the parameter initialization step in the Salp Swarm Algorithm?
a) It initializes the food source position
b) It initializes the population size and other parameters
c) It updates the position of the leader
d) It terminates the algorithm
Answer: b