1. Genetic Algorithm
PRESENTED BY- TAUSEEF AHAMD
M.TECH (COMPUTER SC. & ENGINEERING)
COMPUTER ENGINEERING DEPARTMENT
ZAKIR HUSSAIN COLLEGE OF ENGG. & TECH.
A.M.U, ALIGARH
2. Outlines
A quick overview of GA
Features of GA
Various Methods of Population Selection
Anatomy Of GA
An example of GA
3. References….
Adaptation in Neural and Artificial
Systems, John Holland, 1975.
Genetic Algorithm in Search, Optimization and
Machine Learning, David E. Goldberg, 1989.
C. Darwin. On the Origin of Species by Means of
Natural Selection; or, the Preservation of flavored
Races in the Struggle for Life. John
Murray, London, 1859.
4. A quick overview of GA
Developed: USA in the 1970’s, by John Holland
Holland’s original GA is now known as the simple genetic
algorithm (SGA)
GA was inspired by process of biological evolution
It is based on the Darwin’s theory of “survival of the
fittest” : the better individuals have better chance of
reproducing.
5. Features of GA
Used to solve Hard problems
Maintains a POPULATION of solutions
Solutions are encoded as CHROMOSOMES
REPRODUCTION creates a new population
members
MUTATION and CROSSOVER occurs during
reproduction
9. Population Selection
stochastically select from one generation to
create the basis of the next generation
The requirement is that the fittest individuals
have a greater chance of survival than weaker
ones
fitter individuals will tend to have a better
probability of survival and will go forward to
form the mating pool for the next generation
10. Various Methods of population
Selection
a) Roulette Wheel selection
b) Rank Selection
c) Tournament Selection
d) Elitism
There are many other methods, but we will
discuss briefly only these methods.
12. Fitness f(x) of individual No. 3 is the fittest and
No. 2 is the weakest
Strongest individual a value of 38% and the
weakest 5%
These percentage fitness values can then be used
to configure the roulette wheel
Number of times the roulette wheel is spun is
equal to size of the population
Each time the wheel stops this gives the fitter
individuals the greatest chance of being selected
for the next generation and subsequent mating
pool.
Individual No. 3: 01000001012 will become more
prevalent in the general population because it is
fitter
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14. Tournament Selection
Provides Selective pressure by holding a
tournament competition among n individuals
Best individual from tournament is one having
highest fitness, which is the winner of
tournament
Tournament competitions and winner is then
inserted into mating pool
16. Rank Selection
previous selection will have problems when the
fatnesses differs very much
For example, if the best chromosome fitness is
90% of all the roulette wheel then the other
chromosomes will have very few chances to be
selected
first ranks the population and then every
chromosome receives fitness from this ranking
The worst will have fitness 1, second worst 2 etc.
and the best will have fitness N(number of
chromosomes in population).
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18. Elitism
Copies the best chromosome to new
offspring before the mutation and crossover
When creating a new population by crossover
or mutation the best chromosome might be
lost
Forces GA to retain some numbers of best
individuals at each generation
Has been found that Elitism improves the
performance significantly
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25. An Example
Simple problem: max x2 over {0,1,…,31}
GA approach:
Representation: binary code, e.g. 01101 13
Population size: 4
1-point xover, bitwise mutation
Roulette wheel selection
Random initialisation
We show one generational cycle done by
hand