1. Basics of Genetic Algorithms
General introduction to Genetic Algorithms (GA’s)
Biological background
Origin of species
Natural selection
Genetic Algorithm
Search space
Basic algorithm
Coding
Methods
Examples
Possibilities

2. General Introduction to GA’s
Genetic algorithms (GA’s) are a technique to solve problems which need optimization in search
GA’s are a subclass of Evolutionary Computing
GA’s are based on Darwin’s theory of evolution
History of GA’s
Evolutionary computing evolved in the 1960’s.
GA’s were created by John Holland in the mid-70’s.

3. Biological Background (1) – The cell
Every animal cell is a complex of many small “factories” working together
The center of this all is the cell nucleus
The nucleus contains the genetic information

4. Biological Background (2) – 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
The posibilities of the genes for one property is called: allele
Every gene has an unique position on the chromosome: locus

5. Biological Background (3) – 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.

6. Biological Background (4) – 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.

7. Biological Background (5) – 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

8. Biological Background (6) – Reproduction
During reproduction “errors” occur
Due to these “errors” genetic variation exists
Most important “errors” are:
Recombination (cross-over)
Mutation

9. Biological Background (7) – 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: survival of the fittest.

10. Biological Background (8) – 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

11. Presentation Overview
Purpose of presentation
General introduction to Genetic Algorithms (GA’s)
Biological background
Origin of species
Natural selection
Genetic Algorithm
Search space
Basic algorithm
Coding
Methods
Examples
Possibilities

12. 1 Genetic Algorithm (and GPs) – Search space
Most often one is looking for the best solution in a specific subset of solutions (best?, exploration)
This subset is called the search space (or state space)
Every point in the search space is a possible solution
Therefore every point has a fitness value, depending on the problem definition (higher is closer to best)
GA’s are used to search the search space for the best solution, e.g. a minimum
Difficulties are the local minima and the starting point of the search

13. 2 Genetic Algorithm (and GPs) – Basic algorithm
Starting with a subset of n randomly chosen solutions from the search space (i.e. chromosomes). This is the population
This population is used to produce a next generation of individuals by reproduction
Individuals with a higher fitness have more chance to reproduce (i.e. natural selection)

14. 3 Genetic Algorithm (and GPs) – Basic algorithm
Outline of the basic algorithm
0 START : Create random population of n chromosomes
1 FITNESS : Evaluate fitness f(x) of each chromosome in the population
2 NEW POPULATION (MAIN LOOP)
0 SELECTION : Based on f(x)
1 RECOMBINATION : Cross-over chromosomes
2 MUTATION : Mutate chromosomes
3 ACCEPTATION : Reject or accept new one
3 REPLACE : Replace old with new population: the new
generation (Goto MAIN LOOP)
4 TEST : Test problem criterium
5 LOOP : Continue step 1 – 4 until criterium is satisfied

15. 4 Genetic Algorithm – Coding
Chromosomes are encoded by bitstrings ( GPs – code)
Every bitstring therefore is a solution but not necisseraly the best solution
The way bitstrings can code differs from problem to problem
Either: sequence of on/off or the number 9 1

16. 5 Genetic Algorithm – Coding
Recombination (cross-over) can when using bitstrings schematically be represented:
Using a specific cross-over point 1 1 1 1 X

17. 6 Genetic Algorithm – Coding
Mutation prevents the algorithm to be trapped in a local minimum
In the bitstring approach mutation is simpy the flipping of one of the bits 1 1

18. Genetic Algorithm (8) – Coding
Both recombination and mutation depend a lot on the exact definition of the problem and the choice of representing the chromosomes (e.g. no bitstrings)
Different encodings can be used:
Binary encoding
Permutation encoding
Value encoding
Tree encoding (GPs - if trees are programming code)
Focus in this presentation stays with binary encoding