1. Genetic Algorithms (GAs)
By Mutaz Flmban

2. Outline History of Genetic Algorithm What is the Genetic Algorithm
Biological Background
Basic Genetic Algorithm
Genetic Algorithm Operators
Benefits of Genetic Algorithm
Some Genetic Algorithm Applications Types

3. History of GAs
As early as 1962, John Holland's work on adaptive systems laid the foundation for later developments.
By the 1975, the publication of the book Adaptation in Natural and Artificial Systems, by Holland and his students and colleagues.

4. History of GAs
early to mid-1980s, genetic algorithms were being applied to a broad range of subjects.
In 1992 John Koza has used genetic algorithm to evolve programs to perform certain tasks. He called his method "genetic programming" (GP).

5. What is GA
A genetic algorithm (or GA) is a search technique used in computing to find true or approximate solutions to optimization and search problems.
(GA)s are categorized as global search heuristics.
(GA)s are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).

6. What is GA
The evolution usually starts from a population of randomly generated individuals and happens in generations.
In each generation, the fitness of every individual in the population is evaluated, multiple individuals are selected from the current population (based on their fitness), and modified to form a new population.

7. What is GA
The new population is used in the next iteration of the algorithm.
The algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.

8. Vocabulary Individual - Any possible solution
Population - Group of all individuals
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.

9. Biological Background “The cell”
Every animal cell is a complex of many small “factories” working together.
The nucleus in the center of the cell.
The nucleus contains the genetic information

10. Biological Background “Chromosomes”
Genetic information is stored in the chromosomes
Each chromosome is build of DNA
Chromosomes in humans form pairs.
There are 23 pairs.
The chromosome is divided in parts: genes
Genes code for properties
Every gene has an unique
position on the chromosome:
locus

11. Biological Background “Genetics”
The entire combination of genes is called genotype
A genotype develops to a phenotype
Alleles can be either dominant or recessive
Dominant alleles will always express from the genotype to the phenotype.
Recessive alleles can
survive in the population
for many generations,
without being expressed.

12. Biological Background “Reproduction ”
Reproduction of genetical information
Mitosis
Meiosis
Mitosis is copying the same
genetic information to new
offspring: there is no
exchange of information
Mitosis is the normal way of
growing of multicell structures,
like organs.

13. Biological Background Reproduction
Meiosis is the basis of sexual reproduction
After meiotic division 2 gametes
appear in the process
In reproduction two gametes
conjugate to a zygote wich
will become the new individual
Hence genetic information
is shared between the parents
in order to create new offspring

14. Biological Background
“Reproduction ”
During reproduction “errors” occur
Due to these “errors” genetic variation exists
Most important “errors” are:
Recombination (cross-over)
Mutation

15. Biological Background
“Natural selection”
The origin of species: “Preservation of favourable variations and rejection of unfavourable variations.”
There are more individuals born than can survive, so there is a continuous struggle for life.
Individuals with an advantage have a greater chance for survive: so survival of the fittest.

16. Biological Background
“Natural selection”
Important aspects in natural selection are:
adaptation to the environment
isolation of populations in different groups which cannot mutually mate
If small changes in the genotypes of individuals are expressed easily, especially in small populations, we speak of genetic drift
Mathematical expresses as fitness: success in life

17. Basic Genetic Algorithm
Start with a large “population” of randomly generated “attempted solutions” to a problem
Repeatedly do the following:
Evaluate each of the attempted solutions
Keep a subset of these solutions (the “best” ones)
Use these solutions to generate a new population
Quit when you have a satisfactory solution (or you run out of time)

18. GA Operators Methods of representation Methods of selection
Methods of Reproduction

19. Methods of Representation
Encode solutions as binary strings: sequences of 1's and 0's, where the digit at each position represents the value of some aspect of the solution.
Second approach is encode solutions as arrays of integers or decimal numbers.
A third approach is to represent individuals in a GA as strings of letters, where each letter again stands for a specific aspect of the solution.

20. Methods of Selection
There are many different techniques which a genetic algorithm can use to select the individuals to be copied over into the next generation

21. Methods of Selection Roulette-wheel selection. Elitist selection.
Fitness-proportionate selection.
Scaling selection.
Rank selection.
Generational selection.
Hierarchical selection.

22. Methods of selection
Roulette-wheel selection: the fitter is the solution with the most chances to be chosen
HOW IT WORKS ?

23. Roulette wheel selection
Conceptually, this can be represented as a game of roulette - each individual gets a slice of the wheel, but more fit ones get larger slices than less fit ones.

24. Roulette wheel selection
No.
String
Fitness
% Of Total 1
01101
169
14.4 2
11000
576
49.2 3
01000 64
5.5 4
10011
361
30.9
Total
1170
100.0

25. Roulette wheel selection

26. Another methods of selection
Elitist selection:
The most fit members of each generation are guaranteed to be selected.
Rank selection:
Each individual in the population is assigned a numerical rank based on fitness, and selection is based on this ranking.

27. Methods of Reproduction
Once selection has chosen fit individuals, they must be randomly altered in hopes of improving their fitness for the next generation.

28. Methods of Reproduction
There are two basic strategies to accomplish this:
Crossover
Mutation

29. Methods of Reproduction
Crossover
Two parents produce two offspring
There is a chance that the chromosomes of the two parents are copied unmodified as offspring
There is a chance that the chromosomes of the two parents are randomly recombined (crossover) to form offspring

30. Methods of Reproduction
Crossover
Generating offspring from two selected parents
Single point crossover
Two point crossover (Multi point crossover)
Uniform crossover

31. One-point crossover Randomly one position in the chromosomes is chosen
Child 1 is head of chromosome of parent 1 with tail of chromosome of parent 2
Child 2 is head of 2 with tail of 1
Parents:
Offspring:

32. Two-point crossover
Randomly two positions in the chromosomes are chosen
Avoids that genes at the head and genes at the tail of a chromosome are always split when recombined
Randomly two positions in the chromosomes are chosen
Avoids that genes at the head and genes at the tail of a chromosome are always split when recombined
Parents:
Offspring:

33. Crossover Single point crossover
Cross point
Two point crossover (Multi point crossover) 

34. Uniform crossover A random mask is generated
The mask determines which bits are copied from one parent and which from the other parent
Bit density in mask determines how much material is taken from the other parent (takeover parameter)
Mask: (Randomly generated)
Parents:
Offspring:

35. Methods of Reproduction
Mutation:
Generating new offspring from single parent 

36. A Simple Example The Traveling Salesman Problem:
Find a tour of a given set of cities so that
each city is visited only once
the total distance traveled is minimized

37. Representation Representation is an ordered list of city
numbers known as an order-based GA.
1) London 3) Dunedin ) Beijing 7) Tokyo
2) Venice ) Singapore 6) Phoenix 8) Victoria
CityList1 ( )
CityList2 ( )

38. Crossover Crossover combines inversion and recombination:
* *
Parent ( )
Parent ( )
Child ( )
This operator is called the Order1 crossover.

39. Mutation Mutation involves reordering of the list:
* *
Before: ( )
After: ( )

40. TSP Example: 30 Cities

41. Solution i (Distance = 941)

42. Solution j(Distance = 800)

43. Solution k(Distance = 652)

44. Best Solution (Distance = 420)

45. Benefits of Genetic Algorithms
Concept is easy to understand
Modular, separate from application
Supports multi-objective optimization
Always an answer; answer gets better with time.
Easy to exploit previous or alternate solutions
Flexible building blocks for hybrid applications.

46. GA Applications Domain Application Type Control
Gas pipeline, missile evasion
Design
Aircraft design, keyboard configuration, communication networks
Game playing
Poker, checkers
Security
Encryption and Decryption
Robotics
Trajectory planning

47. references http://en.wikipedia.org/wiki/Genetic_algorithm

48. Questions
Q1) give three methods of selection and explain one of them?.
A1) a) Roulette-wheel selection.
B) Elitist selection.
C) Rank selection.
Q2) what are the genetic algorithm operators?.
A2) a) Methods of representation
b) Methods of selection
c) Methods of Reproduction