1. Introduction to Genetic Algorithm (GA)
Presented By: Rabiya Khalid
Department of Computer Science

2. GA (1/31) Introduction History Based on Darwin’s theory of evolution
Rapidly growing area of artificial intelligence
Used to solve optimization based problems
Techniques inspired by evolutionary biology
Inheritance
Mutation
Selection
Crossover
History
Evolutionary computing evolved in the 1960’s by I.Rechenberg
GA’s were invented by John Holland in the mid-70’s.
Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence.

3. GA (2/31)

4. GA (3/31) Lets learn biology first
Our body is made up of trillions of cells
Each cell has a core structure (nucleus) that contains chromosomes
Each chromosome is made up of tightly coiled strands of deoxyribonucleic acid (DNA)
Genes are segments of DNA that determine specific traits, such as
Eye color
Hair color
A gene mutation is an alteration in DNA
It can be inherited or acquired during your lifetime
Some changes in genes result in genetic disorders.

5. GA (4/31) Natural selection GA is inspired from nature
Only the organisms best adapted to their environment tend to survive
Transmit their genetic characteristics in increasing numbers to succeeding generations
Those less adapted tend to be eliminated
GA is inspired from nature
A genetic algorithm maintains
Population of candidate solutions for the problem
Makes it evolve by iteratively applying a set of stochastic operators

6. GA (5/31)

7. GA (6/31) Key terms Individual Population Search Space Chromosome
Any possible solution
Population
Group of all individuals
Search Space
All possible solutions to the problem
Chromosome
Blueprint for an individual
Trait
Possible aspect (features) of an individual
Allele
Possible settings of trait (black, blond, etc.)
Locus
The position of a gene on the chromosome
Genome
Collection of all chromosomes for an individual

8. GA (7/31) Key terms General use Role in Smart grid Individual
Any possible solution
On-off status of appliances in a time slot
Population
Group of all individuals
Collection of all chromosomes
Search Space
All possible solutions to the problem
Any values in constraint defined limits
Chromosome
Blueprint for an individual
Duplicate values of objective function
Allele
Possible settings of trait (black, blond, etc.)
Characteristics of defined system to satisfy optimization limits
Locus
The position of a gene on the chromosome
Single bit (0 or 1)
Genome
Collection of all chromosomes for an individual
Binary on /off (1/ 0) states arrays for each appliance

9. GA (8/31) GA requirements Representation
A genetic representation of the solution domain (set search space)
A fitness function to evaluate the solution domain (objective function according to system model in case of smart grid)
Representation
Chromosomes could be
Bit strings ( )
Real numbers ( )
Permutations of element (E11 E3 E7 ... E1 E15)
Lists of rules (R1 R2 R3 ... R22 R23)
Program elements (genetic programming)
... any data structure ...

10. GA (9/31) Algorithm Initialization Generate population
Evaluate fitness
While gen<max_gen do
Select two parents
Crossover
Mutation
Elitism
End

11. GA (10/31) Fitness function
The fitness function is always problem dependent
Assigns fitness value to the individual
It summarizes, how close a solution is to achieving the set aims

12. GA (11/31) Selection Darwinian survival of fittest
More preference to get better guys
Ways to select
Roulette wheel
Tournament
Truncation
By itself, pick best
Probability of selection is assigned to individuals based on their fitness value
Two parents are selected randomly from set of individuals with high selection probability
Selected parents are used to produce off-springs

13. GA (12/31) Crossover Single point crossover Two point crossover
Uniform crossover

14. GA (13/31) Mutation Types of mutation
String subjected to mutation after crossover
It prevents falling all solutions into local optimum
It alters bits of chromosomes to make the string better
Bit wise mutation is performed with probability of mutation Po
A random number ro is generated and compared with Po
If Po>ro , then mutation occurs
Types of mutation
Bit inversion
Selected bits are inverted
Order changing
The number are selected and exchanged

15. GA (14/31) Elitism Search space
Elitism involves copying a small proportion of the fittest candidates, unchanged, into the next generation
EA does not waste time re-discovering previously discarded partial solutions
Search space
The set of solutions among which the desired solution resides
Best solution among a number of possible solutions
Represented by one point in the search space

16. GA (15/31) Advantages of GA Limitations
Concepts are easy to understand
Always an answer; answer gets better with time
Less time required for some special applications
Chances of getting optimal solution are more
Limitations
The population considered for the evolution should be moderate or suitable
One for the problem (normally or )
Crossover rate should be 80%-95%
Mutation rate should be low i.e. 0.5%-1% assumed as best
The method of selection should be appropriate
Writing of fitness function must be accurate

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