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Ga presentation
1.
2. As we increase the number of objectives we are trying to
achieve we also increase the number of constrains on the
problem and complixity increases ..
Genatic algorithm GA is ideal for these types of problems
where the search space is large & number of feasiables
solutions is small
3. It’s an adaptive heurastic search algorithm
based on the evoultionary ideas of natural
selections and genatic
It follows the principles laid down by darwin
of survival to the fittest
A fittest fuction is used to evaluate
indviduales
4. • generate intial population M(0)
• compute and save the fitness U(M) of each indvidual m in population
M(T)
• define selection probablties P(M) for each indvidual M in M(T) so that
P(M) is propotional to U(M)
• generate M(T+1) by selecting indviduals from M(T) to mate
• step 2 repeated untill desired character ( sutiable solution ) obtained
8. Any one who can encode solutions of a given problem to chromosomes in
GA & compare the performance ( fitness ) os solutions
Computer architecture : using GA to find out weak links
Learning robots behavior using genatic algorithm
Automated design of mechatronics systems using bond graphs
9. Despite the succeful applications of GA to
numerous optimization of several problems
the identification of the correct settings of
genatic parameters such as ( population size
, crossover & mutation operator ) is not an
easy task
Many works have been performed in order to
identify the correct settings value by trial &
error
10. • fuzzy is capable to adjust the rate of crossover & mutation operators
•Recently CHEONG & LAI (2000) said that GA controlled by fuzzy logic control
are more efficent in search speed & search quality of GA without FLC
11. Based on the fact that it encourages well-
performed operators to produce more
efficient offspring while reducing the chance
of poorly performing operators to destroy the
potential indviduals during genatic search
process
12. We uses a real number representation instead of abit-string
• STEP 1 ) : intial population i.e : any random number of population
•STEP 2) : genetic operator i.e : selection , cross over & mutation
•STEP 3) : stop condition i.e : maximum number of muattion
13. STEP 1) same as CGA.
STEP 2) same as CGA .
STEP 3) RegulatingGA parameters
STEP 4) repeat step 2&3 untill stop condition
is reached .
14.
15. the right selection is one of the most
imoprtant genetic operators .
Learning how to tune the parameters as
( mutation , probablity , population size ...etc)
is important
Because a very small mutation rate may lead
to genetic drift or the high rate of mutation
may lead to loss of good solutions .
16. 1) Choose intial population indvidual
2) Evaluate the fitness of that indvidual
3) Select the best fitness indvidual for matting
4) Breed new indviduals by crossing over
5) Evaluate the new indvidual fitness
Repeat till termination
17. • GA didn’t do well with complexity as the large number of
elements exposed to mutation ie : desiging an engine .
• Sometimes the stop conditions is not clear as the better
solution is only in comparison to other solution .
.
• Operating a dynamic data is difficult as genom start to convert
and become no longer a valid data later
