The presentation covers genetic algorithms, an optimization technique inspired by Darwin's 'survival of the fittest' that employs genetic operators like selection, crossover, and mutation to find optimal solutions for complex problems. It details the terminology, processes involved, and its applications in various fields, as well as the drawbacks of using genetic algorithms. The document concludes with examples of when genetic algorithms are particularly useful.

1. GENETIC
ALGORITHM
@Harsh_Sinha
Presented by :
HARSHWARDHAN SINHA
(3rd SEM C.S.E 2017 -21)
SSIPMT RAIPUR

2. 1.
This Presentation
contains only 25 slides
@Harsh_Sinha

3. We will see
◂ Introduction
◂ Terminology
◂ Flow chart
◂ Advantages, disadvantages
and applications.
◂ Conclude with an example
@Harsh_Sinha

4. GENETIC ALGORITHM
◂ Optimization Algorithm
◂ Nature inspired approach based on
Darwin’s law of “survival of the fittest” and
bio-inspired operators such as Pairing,
crossover and mutation
◂ Frequently used to find optimal or near-
optimal solutions of difficult problems
4@Harsh_Sinha

5. GENETIC ALGORITHM
◂ It is not an smart algorithm neither an
intelligence algorithm
Rather it reflects the changes and
response to it very quickly, so it called
Genetic algorithm.
5@Harsh_Sinha

6. OPTIMIZATION
◂ Optimization is the process of making
something better
◂ Finding the values of inputs in such a way
that we get the “best” output value.
6@Harsh_Sinha

7. TERMINOLOGY
◂ Population
◂ Chromosomes
◂ Genes
7
1 0 1 1 0 1
Population
Genes
chromosomes
Alleles
@Harsh_Sinha

8. BRIEF OF TERMINOLOGY
◂ Genes : A gene represents some
data.
◂ Chromosomes : A chromosome
is an array of genes. In some way
it contains information about
solution which it represents thus
it requires encoding
◂ Population: Collection of
chromosomes strings/array.
8
1 0 1 1 0 1
0 0 1 1 1 0
1 1 0 1 0 0
1 0 0 0 0 1
1 1 0 1 1 1
1 0 1 0 1 1
@Harsh_Sinha

9. FLOW CHART
9@Harsh_Sinha
END

10. GENETIC OPERATORS
◂ Selection
◂ Crossover
◂ Mutation
10@Harsh_Sinha

11. SELECTION
Selection individual for creating the next
generation(better generation).
In terms of CS selecting the data so to reach optimal
solution.
Selection is done by applying fitness function.
11@Harsh_Sinha

12. FITNESS & FITNESS FUNCTION
Fitness: The value assigned to an individual based on how far or
close an individual is from solution; greater the fitness value
better the solution it contains.
Fitness Function: A function that assigns fitness value to the
individual.It is problem specific.
12@Harsh_Sinha

13. FLOW CHART
13
By applying
fitness
function we
select the
most
promising
element
@Harsh_Sinha
ENDEND

14. HOW ARE PARENTS SELECTED?
1. Roulette wheel Selection
14
chromosomes Fitness
value
A 9.8
B 7.9
C 2.4
D 4.5
SPIN THE
WHEEL
@Harsh_Sinha

15. HOW ARE PARENTS SELECTED?
2. Rank Selection
Remove the concept of fitness
value while selecting a parent.
Every individual in the
population is ranked according
to their fitness.
15
chromosomes Fitness
value
Rank
A 9.8 1
B 7.9 2
C 2.4 4
D 4.5 3
@Harsh_Sinha

16. HOW ARE PARENTS SELECTED?
3. STOCHASTIC UNIVERSAL SAMPLING(SUS): Multiple fixed points,
all the parents are chosen in just one spin of the wheel.
4.TOURNAMENT SELECTION: Select k individuals from the
population at random and select the best out of these to become a
parent, same process is repeated for selecting the next parent.
16@Harsh_Sinha

17. FLOW CHART
17@Harsh_Sinha
ENDEND

18. CROSSOVER
Crossover is a genetic operator that combines(mates) two
chromosomes(parents) to produce a new chromosome(offspring).
The crossover operators are of many types:
1. One simple ways is one-point crossover.
2. The others are two-point, uniform, arithmetic and heuristic
crossovers.
They are selected based on the way chromosomes are encoded.
(Encoded because it is computer science and not biology.)
18@Harsh_Sinha

19. CROSSOVER
ONE-POINT CROSSOVER: A random crossover point is
selected and the tail of its two parents are swapped to get new
offsprings.
19
1 0 1 1 1 0 1 1
1 1 1 0 1 0 0 1
1 0 1 0 1 0 0 1
1 1 1 1 1 0 1 1
Parent Chromosomes Offspring Chromosomes
@Harsh_Sinha

20. FLOW CHART
20@Harsh_Sinha
ENDEND

21. MUTATION
◂ Small random tweak in the chromosome, to get a new solution.
Types
1. Bit-flip mutation: Select one or more random bits and flip them.
1. Swap mutation: Select two random bits and swap them.
21
0 1 1 0 1 0 0 1 0 1 0 0 1 1 0 1
1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 0
@Harsh_Sinha

22. 22
Determines which
individual are to be
kicked out and
which are to be
kept in the next
generation.
SURVIVOR SELECTION
@Harsh_Sinha
END

23. When are Genetic Algorithms Useful?
There are at least three situations where genetic algorithms are useful:
1. The objective function is not smooth (i.e., not differentiable).
2. There are multiple local optima.
3. Useful when the search space is very large and there are a large
number of parameters (the meaning of “large” keeps changing).
4. Provides a list of “good” solutions and not just a single solution
For details about each point refer to: https://www.burns-stat.com/documents/tutorials/an-introduction-to-genetic-
algorithms/
23@Harsh_Sinha

24. ITS APPLICATION
1. Optimized Telecommunications Routing
2. Trip, Traffic and Shipment Routing
3. Encryption and Code Breaking
4. Evolvable Hardware
5. Joke and Pun Generation
These are few of them.
For more we can refer to : https://www.brainz.org/15-real-world-applications-genetic-algorithms/
24@Harsh_Sinha

25. Drawbacks
1. Computationally expensive as fitness value is calculated repeatedly
2. Not suited for all problems, especially problems which are
simple and for which derivative information are available
3. GA may not converge to the optimal solutions,if not implemented
properly.
25@Harsh_Sinha

26. 26
Thanks!
@Harsh_Sinha