Upcoming SlideShare
×

# Ai presentation

384 views

Published on

Published in: Technology
0 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

• Be the first to like this

Views
Total views
384
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
13
0
Likes
0
Embeds 0
No embeds

No notes for slide

### Ai presentation

1. 1. CS1103
2. 2. GROUP MEMBERS DARAKSHAN ANJUM 11060819-008 IQRA AMJAD 11060819-041 CS1103
3. 3. GENETIC ALGORITHM CS1103
4. 4. INTRODUCTION •Genetic algorithms are a part of evolutionary computing •Genetic algorithms operate on a set of possible solutions.
5. 5. CONTI... solutions found by an algorithm can be good, poor, or infeasible . A way to specify how good that solution is. - by assigning a fitness value [or just fitness] to the solution. Can you guess, genetic algorithms are inspired by Darwin's theory about evolution? - solution to a problem solved by genetic algorithms is evolved. Introduction 5
6. 6. DESCRIPTION - Algorithm is started with a set of solutions called population. - Solutions from one population are taken and used to form a new population. . Solutions which are selected to form new solutions are selected - according to their fitness - the more suitable they are the more chances they have to reproduce. 6
7. 7. CONTI... -This is repeated until some condition (for example number of populations or improvement of the best solution) is satisfied. 7
8. 8. WORKING OF GA 1. [Start] Generate random population of n chromosomes (suitable solutions for the problem) . 2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the population . 3. [New population] Create a new population by repeating following steps until the new population is complete 8
9. 9. CONTI... [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected) [Crossover] With a crossover probability cross over the parents to form a new offspring (children). If no crossover was performed, offspring is an exact copy of parents. [Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome). [Accepting] Place new offspring in a new population 9
10. 10. CONTI... 4. [Replace] Use new generated population for a further run of algorithm 5. [Test] If the end condition is satisfied, stop, and return the best solution in current population 6. [Loop] Go to step 2 CS1104-11 S-R Latch 10
11. 11. GA OPERATORS 1. Crossover 2. Mutation Encoding of chromosome Chromosome 1 : 1101100100110110 Chromosome 2 : 1101111000011110 CS1104-11 S-R Latch 11
12. 12. CONTI... CROSSOVER: Crossover can then look like this ( | is the crossover point): Chromosome 1 Chromosome 2 : : 11011 | 00100110110 11011 | 11000011110 Offspring 1 Offspring 2 : : 11011|11000011110 11011 |00100110110 CS1104-11 S-R Latch 12
13. 13. CONTI… MUTATION: After a crossover is performed, mutation take place. Original offspring 1 Original offspring 2 : : 1101111000011110 1101100100110110 Mutated offspring 1 Mutated offspring 2 : : 1100111000011110 1101101100110110 13
14. 14. GA EXAMPLE 14
15. 15. 15
16. 16. 16