M3

Question 1

Genetic Algorithm is part of the broader soft computing paradigm known as ___

Answer 1

evolutionary computation

Question 2

Genetic Algorithm was first introduced by Holland in ___

Answer 2

1975

Question 3

Genetic Algorithm methodology is particularly suited for ___

Answer 3

optimization

Question 4

In genetic algorithm, a population of candidate solutions are repeatedly altered until an ___ is found

Answer 4

optimal solution

Question 5

The step 2 of genetic algorithm is to select a population of better solutions (next generation) by using a ___ (fitness evaluation function)

Answer 5

measure of goodness

Question 6

In genetic algorithm, a series of genes, known as a __, represents one possible solution

Answer 6

chromosome

Question 7

In genetic algorithm, each gene in the chromosome represents one ___ of the solution pattern.

Answer 7

component

Question 8

In GA, each gene can have one of a number of possible values known as ___

Answer 8

alleles

Question 9

In GA, the process of converting a solution from its original form into a chromosome is known as ____.

Answer 9

coding

Question 10

In GA, the most common form of representing a solution as a chromosome is a string of ___ digits (aka a ___ vector)

Answer 10

binary

Question 11

In GA, solution bit strings are ___ to enable their evaluation using a fitness measure

Answer 11

decoded

Question 12

In GA, only the fittest survive and contribute to the gene ___ of the next generation

Answer 12

pool

Question 13

In GA, each chromosome’s likelihood of being selected is ___ to its fitness value.

Answer 13

proportional

Question 14

In GA, solutions failing selection are “___”, and are discarded

Answer 14

bad

Question 15

___refines good solutions from current generation to produce next generation of solutions

Answer 15

Alteration

Question 16

___ is done by splicing two chromosomes at a crossover point and swapping the spliced parts

Question 17

Crossover is done by splicing two chromosomes at a ___and swapping the spliced parts

Answer 17

crossover point

Question 18

Crossover carried out with a probability – typically ___

Answer 18

0.7

Question 19

Chromosomes not crossed over are ___

Answer 19

cloned

Question 20

___ is a random adjustment in the genetic composition

Answer 20

Mutation

Question 21

In mutation, probability are kept ___: typically 0.001 to 0.01

Answer 21

low

Question 22

Mutation are may be ___

Answer 22

counterproductive

Question 23

In GA, binary bit strings can produce “__” chromosomes needing repair

Answer 23

faulty

Question 24

There was an increasing number of industrial and business applications of GA since late ___

Answer 24

1980s

Question 25

___ project in Europe demonstrated potential of GA technology in a broad range of business applications including o Insurance risk assessment o Economic modelling o Credit scoring o Direct marketing

Answer 25

PAPAGEN

Question 26

___ is an investment firm in California that started using GA technique in 1993

Answer 26

First Quadrant

Question 27

GA can act as an alternative to ___ if o number of rules is too large or o the nature of the knowledge-base too dynamic

Answer 27

Expert Systems

Question 28

___ are regions that hold good solutions relative to regions around them, but which do not necessarily contain the best overall solutions

Answer 28

Local maxima

Question 29

A GA is said to have converged prematurely if it explores a ___ extensively

Answer 29

local maximum

Question 30

In GA, most significant factor leading to premature convergence is a ___ which is too slow

Answer 30

mutation rate

Question 31

___ happens when the mutation rate is too high. GAs change too often and can’t hold onto good solutions — they get lost.

Answer 31

Mutation Interference