Genetic Algorithm By HNematzadeh Objectives To understand the processes involved ie GAs Basic flows operator and parameters roles effects etc To be able to apply GAs in solving optimisation problems ID: 563739
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Slide1
Advanced AI – Session 6
Genetic Algorithm
By:
H.NematzadehSlide2
Objectives
To understand the processes involved
ie
.
GAs
Basic flows –operator and parameters (roles, effects etc)
To be able to apply
GAs
in solving optimisation problemsSlide3
Evolutionary computation
we are products of evolution, and thus by modelling the process of evolution, we might expect to create intelligent behaviour. Evolutionary computation simulates evolution on a computer. The result of such a simulation is a series of optimisation algorithms, usually based on a simple set of rules. Optimisation iteratively improves the quality of solutions until an optimal, or at least feasible, solution is found.Slide4
Nature like evolution is slow
Evolution is a tortuously
slow
process from the human perspective, but the simulation of evolution on a computer does not take billions of years!Slide5
Natural evolution
Evolution can be seen as a process leading to the maintenance of a population’s ability to
survive and reproduce
in a specific environment. This ability is called
evolutionary fitness
.
Evolutionary
fitness
can also be viewed as a
measure
of organism’s ability to anticipate changes in its environment.
The better an organism's fitness to the environment, the better its chances to surviveSlide6
Rabbits & FoxesSlide7
Encoding Vs EvaluationSlide8
Class of searches techniquesSlide9
Evolutionary ProcessSlide10
Mice & Cats: an evolutionary problemSlide11
The mice & cat algorithmSlide12
General evolution processSlide13
GA Vs Real lifeSlide14
Basic GASlide15
Basic GASlide16
Another way of looking at this…Slide17
Flowchart of GASlide18
Another way of looking at this…Slide19
GA ProcessSlide20
Example 1 (not included in the book) burger and profit problemSlide21
AnalysisSlide22
Fitness EvaluationSlide23
Selection Slide24
Crossover Slide25
Mutation Slide26
After 1st
runSlide27
Example 2: optimization of a one variable functionSlide28
Steps in GA developmentSlide29
The entire universe of discourseSlide30
Operator parametersSlide31
Fitness functionSlide32
The fitness functions and chromosomes locationSlide33
Selection using roulette wheel
One of the most commonly used chromosome selection techniques is the
roulette wheel selection
(Goldberg, 1989; Davis, 1991). Figure 7.4 illustrates the roulette wheel for our example. As you can see, each chromosome is given a slice of a circular roulette wheel.Slide34
Selection using roulette wheelSlide35
Crossover function Slide36
Mutation functionSlide37
GA cycleSlide38
Example 3- 2 variables function