2. Artificial Intelligence Methods (AIM)
University of Kurdistan
Artificial Intelligence Methods (AIM)
Lecture 2: Genetics Algorithms (GA’s)
Lecturer:
Kaveh Mollazade, Ph.D.
Department of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, IRAN.

3. This lecture will cover:
Contents
This lecture will cover:
Introduction
History
Biological background
GA operators
Examples 1

4. Introduction
Genetic algorithms (GA’s) are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence.
Genetics algorithms are a technique to solve problems which need optimization based on idea that evolution represents search for optimum solution set.
GA’s are based on Darwin’s theory of evolution. Simply said, solution to a problem solved by genetic algorithms is evolved. 2

5. Darwin’s theory of evolution3

6. History
Idea of evolutionary computing was introduced in the 1960s by I. Rechenberg in his work "Evolution strategies“.
Genetic Algorithms (GAs) were invented by John Holland and developed by him and his students and colleagues. This lead to Holland's book "Adaption in Natural and Artificial Systems" published in 1975.
The 2006 NASA ST5 spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an Evolved antenna. 4

7. Our world 5 It is changing continuously.
Each animate who wants to live in the world should adapt himself to the environment conditions.
The process of adaptation to the environment is known as evolution. 5

8. Different kinds of evolution
Natural selection
Reproduction
Mutation
Symbiosis 6

9. Evolution examples 7

10. Genetics: the manuscript of nature
To do the optimization properly, the nature should keep the information obtained during the million years ago.
Chemical elements are the basis of animates. The nature uses these elements to save the information of biological species. 8

11. Biological inspiration
Cell:
The cell is the basic structural, functional, and biological unit of all known living organisms. Cells are the smallest unit of life that can replicate independently, and are often called the "building blocks of life". The nucleus, located in the center of cell, contains the genetic information. 9

12. Biological inspiration
Chromosome:
Chromosomes are thread-like structures located inside the nucleus of animal and plant cells. Each chromosome is made of protein and a single molecule of deoxyribonucleic acid (DNA). Passed from parents to offspring, DNA contains the specific instructions that make each type of living creature unique. 10

13. Biological inspiration
DNA structure:
A DNA molecule is like a helical ladder in which the main branches (backbone) is made up sugar-phosphate strips. The ladder base is made up of four different chemical compounds called nucleotide or organic bases. 11

14. TA AT GC CG Biological inspiration 12 DNA structure:
Four different kind of combinations, which require a hydrogen compound, is possible to made. TA AT GC CG 12

15. Biological inspiration
Gene:
The chromosome is divided in parts called genes. A gene is the basic physical and functional unit of heredity. Genes act as instructions to make molecules called proteins. Every gene has a unique position on the chromosome that is called locus. 13

16. Biological inspiration
Phenotype vs genotype:
Phenotype
Genotype 14

17. An important conclusion
There is a unique relationship between an animate and its gene sequences. This mean each animate has a fixed unique DNA structure which is not repeated in other animates.
This phenomenon is known as coding. The phenotype holds the main information of an animate and DNA sequence is its coding. 15

18. Search space
If we are solving some problem, we are usually looking for some solution, which will be the best among others. The space of all feasible solutions (it means objects among those the desired solution is) is called search space (also state space). Each point in the search space represent one feasible solution. Each feasible solution can be "marked" by its value or fitness for the problem. We are looking for our solution, which is one point (or more) among feasible solutions - that is one point in the search space.
The looking for a solution is then equal to a looking for some extreme (minimum or maximum) in the search space. The search space can be whole known by the time of solving a problem, but usually we know only a few points from it and we are generating other points as the process of finding solution continues. 16

19. 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). 17

20. Basic genetic algorithm18

21. GA operators Methods of representation Methods of selection
Methods of reproduction 19

22. GA operators: 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

23. GA operators: 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.
Roulette-wheel selection
Elitist selection
Fitness-proportionate selection
Scaling selection
Rank selection
Generational selection
Hierarchical selection 21

24. GA operators: methods of selection
Roulette-wheel selection:
The fitter is the solution with the most chances to be chosen.
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. 22

25. GA operators: methods of selection
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 23

26. GA operators: methods of selection
Roulette-wheel selection: 24

27. GA operators: methods of reproduction
Once selection has chosen fit individuals, they must be randomly altered in hopes of improving their fitness for the next generation.
There are two basic strategies to accomplish this:
Crossover
Mutation 25

28. GA operators: 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.
Generating offspring from two selected parents:
Single (one) point crossover
Two point crossover (Multi point crossover)
Uniform crossover 26

29. GA operators: methods of reproduction
Single (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. 27

30. GA operators: methods of reproduction
Two (multi) 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. 28

31. GA operators: methods of reproduction
Single (one) point crossover: 
Cross point
Two (multi) point crossover: 
Cross point
Cross point 29

32. GA operators: methods of reproduction
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). 30

33. GA operators: methods of reproduction
Mutation:
Generating new offspring from single parent 31

34. GA implementation process1
selection
cross over
Search
space 1 1
population A B C D
mutation
Fitness
evaluation 1
reproduction 32

35. GA implementation process33

36. Summary We have learned: Definition of genetics algorithm
Biological background of GA.
Principles of GA operators
Some practical examples (exercise in the class) 34

37. Kurdistan nature
Winter