1. Evolutionary Computation
Artificial Intelligence
Department of Industrial Engineering and Management
Cheng Shiu University

2. Outline Can evolution be intelligent? Simulation of natural evolution
Natural Genetics
Evolutionary Cycle
Developments of Evolutionary computation
Ideas of Evolutionary methods
Essential Elements of EC

3. Can evolution be intelligent?
Intelligence can be defined as the capability of a system to adapt its behaviour to ever-changing environment. According to Alan Turing, the form or appearance of a system is irrelevant to its intelligence.
Evolutionary computation simulates evolution on a computer. The result of such a simulation is a series of optimisation algorithms, usually based on a simple set of rules. Optimisation iteratively improves the quality of solutions until an optimal, or at least feasible, solution is found.

4. The behaviour of an individual organism is an inductive inference about some yet unknown aspects of its environment. If, over successive generations, the organism survives, we can say that this organism is capable of learning to predict changes in its environment.
The evolutionary approach is based on computational models of natural selection and genetics. We call them evolutionary computation, an umbrella term that combines genetic algorithms, evolution strategies and genetic programming.

5. Simulation of natural evolution
On 1 July 1858, Charles Darwin presented his theory of evolution before the Linnean Society of London. This day marks the beginning of a revolution in biology.
Darwin’s classical theory of evolution, together with Weismann’s theory of natural selection and Mendel’s concept of genetics, now represent the neo-Darwinian paradigm.

6. Neo-Darwinism is based on processes of reproduction, mutation, competition and selection. The power to reproduce appears to be an essential property of life. The power to mutate is also guaranteed in any living organism that reproduces itself in a continuously changing environment. Processes of competition and selection normally take place in the natural world, where expanding populations of different species are limited by a finite space.

7. Evolution can be seen as a process leading to the maintenance of a population’s ability to survive and reproduce in a specific environment. This ability is called evolutionary fitness.
Evolutionary fitness can also be viewed as a measure of the organism’s ability to anticipate changes in its environment.
The fitness, or the quantitative measure of the ability to predict environmental changes and respond adequately, can be considered as the quality that is optimised in natural life.

8. How is a population with increasing fitness generated?
Let us consider a population of rabbits. Some rabbits are faster than others, and we may say that these rabbits possess superior fitness, because they have a greater chance of avoiding foxes, surviving and then breeding.
If two parents have superior fitness, there is a good chance that a combination of their genes will produce an offspring with even higher fitness. Over time the entire population of rabbits becomes faster to meet their environmental challenges in the face of foxes.

9. Natural Genetics
The information required to build a living organism is coded in the DNA and other genetic material found in the cells of that organism
Within a species, most of the genetic material is the same
Small changes in the genetic material give rise to small changes in the organism
E.g height, hair colour

10. -A-B-A-D-C-B-B-C-C-A-D-B-C-C-A-
DNA and Genes
DNA is a large molecule made up of fragments. There are several fragment types, each one acting like a letter in a long coded message:
-A-B-A-D-C-B-B-C-C-A-D-B-C-C-A-
Certain groups of letters are meaningful together - a bit like words.
These groups are called genes
The DNA is made up of genes and rubbish
A,T,C,G

11. Example: Human Reproduction
Human DNA is organised into chromosomes
Most human cells contains 23 pairs of chromosomes which together define the physical attributes of the individual:

12. Reproductive Cells
Sperm and egg cells contain 23 individual chromosomes rather than 23 pairs
Reproductive cells are formed by one cell splitting into two
During this process the pairs of chromosome undergo an operation called crossover

13. Crossover
During crossover the chromosome pairs link up and swap parts of themselves:
Before After
After crossover one of each pair goes into each cell

14. Fertilisation Sperm cell from Father Egg cell from Mother
New person cell

15. Mutation
Occasionally some of the genetic material changes very slightly during this process
This means that the child might have genetic material information not inherited from either parent
This is most likely to be catastrophic

16. The Evolutionary Cycle
Selection
Parents
Evaluation
Recombination
Population
Mutation
Replacement
Offspring

17. Simulation of natural evolution
All methods of evolutionary computation simulate natural evolution by creating a population of individuals, evaluating their fitness, generating a new population through genetic operations, and repeating this process a number of times.
We will start with Genetic Algorithms (GAs) as most of the other evolutionary algorithms can be viewed as variations of genetic algorithms.

18. CI and Evolutionary Algorithms

19. Genetic Algorithms Evolutionary Programming Genetic Programming +
50‘s and 60‘s
The Michigan School
The German School
The California School
(Holland)
(Rechenberg, Schwefel, Bienert)
(Fogel, Owens, Walsh)
- More emphasis on recombination, inversion, etc.
- Used the term “genetics”
- More emphasis on selection and mutation
- Used the term “evolution”
evolution strategies
simulated evolution
Genetic
Algorithms
Evolution
Strategies
Evolutionary
Programming t o d a y
Genetic
Programming +
(Koza et al.):
Ideas of the Michigan School
put to automated programming

20. History 1859 Charles Darwin: inheritance, variation, natural selection
1957 G. E. P. Box: random mutation & selection for optimization
1958 Fraser, Bremermann: computer simulation of evolution
1964 Rechenberg, Schwefel: mutation & selection
1966 Fogel et al.: evolving automata - “evolutionary programming”
1975 Holland: crossover, mutation & selection - “reproductive plan”
1975 De Jong: parameter optimization - “genetic algorithm”
1989 Goldberg: first textbook
1991 Davis: first handbook
1993 Koza: evolving LISP programs - “genetic programming”

21. Historical Perspective
Evolutionary computation
Invented 10 times independently from
Early pioneers included Barricelli, Bledsoe, Bremermann, Box, Conrad, Fogel, Fraser, Friedberg, Holland, Kaufman, Rechenberg, Schwefel and others.

22. Evolutionary Computation
Fraser(1957); Bremmerman (1958)
Fogel (1962)
Fogel et al (1966)
Schwefel &
Rechenberg (1965)
Holland (1975)
Schwefel (1981)
Goldberg (1989)
D. Fogel (1992)
Koza(1992)

24. General Ideas of Evolutionary Methods
EA’s use nature as an inspiration
the principles of evolution
birth and death of individuals in a population
survival of the fittest
multiple generations
mating or ‘crossover’ (two parents creating one offspring)
mutation (random variation) programming
Birth and death may have nothing to do with real life, but can refer to ideas, strategies,…

25. The Evolutionary Metaphor
Individual
Fitness
Environment
PROBLEM SOLVING
Candidate Solution
Quality
Problem

26. A Pseudo Program of Evolutionary Algorithms
begin
t : = 0 ;
initialize P( t ) ;
While not terminate do
evaluate P( t ) ;
M( t ) : = select-mates( P( t ) ) ;
O( t ) : = alteration( M( t ) ) ;
evaluate( O( t ) ) ;
P( t+1 ) : = select( O( t )∪P( t ) ) ;
t : = t+1 ;
end

27. A Pseudo Program of Evolutionary Computation (2)
Evolutionary computation starts with an initialization of a population of individuals (solution candidates), called P(0), with a population size to be supplied by the users.
These solution will then be evaluated based an objective function or a fitness function determined by the problem we encounter.
We may cite Robert Axtell’s study to indicate the limit of population size.

28. A Pseudo Program of Evolutionary Computation (3)
The continuation of the procedure will hinges on the termination criteria supplied by users.
Two major operators are conducted to form the new generation, which can be as considered as a correspondence as follows:

29. Three Stages First Stage: making a mating pool (M(t))
Second Stage: generating offspring (O(t))
Third Stage: forming new generation (P(t+1))

30. Pseudo code of an Evolutionary Algorithm
begin EA;
t = 0; // Initializing time
// Initialize a usually random population of individuals:
random P( t );
// Evaluate the fitness of all individuals:
evaluate P( t );
while not done do // Testing for termination criterion
t = t + 1; // Increasing time
// Select a sub-population for offspring production:
P' = select P( t );
// Recombine the “genes” of selected parents:
recombine P'( t );
// Stochastically perturb genes of the mated population:
mutate P'( t );
// Evaluate the new fitness:
evaluate P'( t );
// Select the survivors from actual fitness:
P = survive P( t ), P'( t )
endwhile;
end EA;

31. Pseudo Code of an Evolutionary Algorithm
Create initial random population
Evaluate fitness of each individual
yes
Termination criteria satisfied ?
stop no
Select parents according to fitness
Recombine parents to generate offspring
Mutate offspring
Replace population by new offspring

32. The Evolutionary Cycle
initialize population
evaluate
select mating partners
(terminate)
recombinate
loop
select
mutate
evaluate

33. Parents
Selection
Recombination
Mutation
Offspring
Population
Replacement

34. Object problem and Fitness
solution
genotype s M f
fitness
object problem

35. Five Essential Elements of EC
individuals and their representations,
population size,
selection,
mutation,
recombination.

36. Five Essential Elements of EC
reproduction
t + 1
selection
recombination
mutation