M3 Flashcards
Genetic Algorithm is part of the broader soft computing paradigm known as ___
evolutionary computation
Genetic Algorithm was first introduced by Holland in ___
1975
Genetic Algorithm methodology is particularly suited for ___
optimization
In genetic algorithm, a population of candidate solutions are repeatedly altered until an ___ is found
optimal solution
The step 2 of genetic algorithm is to select a population of better solutions (next generation) by using a ___ (fitness evaluation function)
measure of goodness
In genetic algorithm, a series of genes, known as a __, represents one possible solution
chromosome
In genetic algorithm, each gene in the chromosome represents one ___ of the solution pattern.
component
In GA, each gene can have one of a number of possible values known as ___
alleles
In GA, the process of converting a solution from its original form into a chromosome is known as ____.
coding
In GA, the most common form of representing a solution as a chromosome is a string of ___ digits (aka a ___ vector)
binary
In GA, solution bit strings are ___ to enable their evaluation using a fitness measure
decoded
In GA, only the fittest survive and contribute to the gene ___ of the next generation
pool
In GA, each chromosome’s likelihood of being selected is ___ to its fitness value.
proportional
In GA, solutions failing selection are “___”, and are discarded
bad
___refines good solutions from current generation to produce next generation of solutions
Alteration
___ is done by splicing two chromosomes at a crossover point and swapping the spliced parts
Crossover is done by splicing two chromosomes at a ___and swapping the spliced parts
crossover point
Crossover carried out with a probability – typically ___
0.7
Chromosomes not crossed over are ___
cloned
___ is a random adjustment in the genetic composition
Mutation
In mutation, probability are kept ___: typically 0.001 to 0.01
low
Mutation are may be ___
counterproductive
In GA, binary bit strings can produce “__” chromosomes needing repair
faulty
There was an increasing number of industrial and business applications of GA since late ___
1980s
___ 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
PAPAGEN
___ is an investment firm in California that started using GA technique in 1993
First Quadrant
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
Expert Systems
___ are regions that hold good solutions relative to regions around them, but which do not necessarily contain the best overall solutions
Local maxima
A GA is said to have converged prematurely if it explores a ___ extensively
local maximum
In GA, most significant factor leading to premature convergence is a ___ which is too slow
mutation rate
___ happens when the mutation rate is too high. GAs change too often and can’t hold onto good solutions — they get lost.
Mutation Interference
