Test
Data Science Various Topics
Crea una cuenta gratis para terminar este test
Guarda tu progreso y consulta tu resultado final.
¿Ya tienes cuenta? Inicia sesión
Preguntas del test (7)
1Is a genetic algorithm a type of evolutionary algorithm?
- ANo. An evolutionary algorithm is a type of genetic algorithm.
- BYes. A genetic algorithm is a type of evolutionary algorithm.
2Which of the following best describes how a genetic algorithm works?
- A1. Generates a random population of n solutions.
2. Evaluates the fitness of each solution
3. Creates a new population by implementing the steps below
a) SELECTION. Select 2 parent solutions based on their fitness (better fitness = better chance of being selected)
b) CROSSOVER. Based on a crossover probability, and a crossover approach, use the details of the 2 parent solutions to create 2 new solutions (children/offspring).
c) MUTATION: Based on a mutation probability, mutate some details for some of the offspring
d) ACCEPTANCE: place the new offspring into the new population
4. Uses the new population for the next iteration of the algorithm
5. Stop when a stopping condition is met - B1. Generates a population of n solutions based on all possible combinations of the details associated with the population.
2. Evaluates the fitness of each solution
3. Creates a new population by implementing the steps below
a) SELECTION. Select 2 parent solutions based on their fitness (better fitness = better chance of being selected)
b) CROSSOVER. Based on a crossover probability, and a crossover approach, use the details of the 2 parent solutions to create 1 new solution (child/offspring).
c) MUTATION: Based on a mutation probability, mutate some details for some of the offspring
d) ACCEPTANCE: place the new offspring into the new population
4. Uses the previous population AND the new population for the next iteration of the algorithm
5. Stop when a stopping condition is met
- A1. Generates a random population of n solutions.
3Which of the following are correct?
- AA two point crossover approach takes a random selection of 2 of a parent's values and applies it to all offspring.
- BA two point crossover approach takes the parent's values between the two points and applies it to one of the offspring. The 2nd parent's values between the 2 points are used for the other offspring.
- CA uniform crossover approach takes a random value from a parent and applies it to both offspring.
- DA uniform crossover approach randomly selects a parent based on a 50/50 probability and assigns the parent's first value to the 1st child, and the inverse to the 2nd child. This is repeated for each value.
- EA one point crossover approach randomly chooses a point after which all those values are copied from the parent to one child, and all the values before that point are copied from the first parent, and all values after that point are copied from the second parent.
4True or False: A genetic algorithm supports multi-object optimisation.
- AVerdadero
- BFalso
5True or False: A genetic algorithm is inherently parallel and easily distributed.
- AVerdadero
- BFalso
6True or False: Its easy to determine genetic algorithm parameters such as population size, fitness function, and mutation probability
- AVerdadero
- BFalso
7Which of the following are Fitness Proportionate methods used in genetic algorithms?
- ATournament
- BRoulette Wheel
- CRank
Información
- Descripción
- Test sobre Data Science Various Topics, creado por Nicola Fox el 26/03/2020.
- Autor
- Nicola Fox
- Idioma
- English
- Visitas
- 1
- Valoración
- Aún sin valoraciones Valora este test