Genetic Algorithms Concepts and Techniques

The video tutorial explains genetic algorithms in machine learning, inspired by Darwin's theory of natural selection. It covers the five phases: initialization, fitness assignment, selection, crossover, and termination. The tutorial details how initial populations are formed, how fitness functions determine the fittest individuals, and how crossover and mutation create new offspring. Termination occurs when the population converges. The video also highlights real-world applications of genetic algorithms.

What inspired the development of genetic algorithms?

• Newton's laws of motion

• Einstein's theory of relativity

• Darwin's theory of evolution

• Mendel's laws of inheritance

Which of the following is NOT a phase in genetic algorithms?

• Initialization

• Fitness assignment

• Data mining

• Crossover

What is the initial set of individuals in a genetic algorithm called?

• Offspring

• Population

• Genes

• Chromosomes

How is the fitness of an individual determined in genetic algorithms?

• By its age

• Using a fitness function

• Through random selection

• By its size

Which method is NOT used for selecting individuals in genetic algorithms?

• Random guessing

• Rank-based reselection

• Tournament selection

• Relative wheel selection

What is the purpose of the crossover phase in genetic algorithms?

• To eliminate weak individuals

• To assign fitness values

• To generate new offspring

• To mutate genes

What is a common mutation technique in genetic algorithms?

• Gene duplication

• Flipping

• Chromosome deletion

• Gene inversion

When does a genetic algorithm typically terminate?

• When the population size exceeds a limit

• When the population has converged

• After a fixed number of generations

• When all individuals are identical

Which mutation method involves swapping genes?

• Insertion mutation

• Exchange or swap mutation

• Gaussian mutation

• Flipping

What happens if the termination condition is not met in a genetic algorithm?

• The algorithm restarts from scratch

• The algorithm switches to a different problem

• The algorithm continues with selection and crossover

• The algorithm stops immediately