1. Evolutionary Algorithms

2. Overview Motivation Nature as a Standard Genetic Algorithms
Genetic Programming
Evolutionary Strategies
Conclusion

3. Motivation
Since millions of years creatures populate Earth
By changes in the biosphere there are again and again new environmental conditions
Populations had to learn to adapt to the new conditions; permanent stepwise development, few stagnancy
Organisms are optimally adapted with respect to their needs
Nature has its own laws, rules, strategies, and mechanisms
Evolution: successful, robust mechanism, allows creatures over generations to adapt to environmental conditions
Goal of evolution is not predefined; optimisation, innovation, creativity
Selection factors: competition, food supply, enemies, climate, environment, via human beings additionally breed,

4. Overview Motivation Nature as a Standard Genetic Algorithms
Genetic Programming
Evolutionary Strategies
Conclusion

5. Nature as a Standard - Evolution, Genome
Lamarck's thesis (1809): Adaptation, urge to perfection (by specific needs) spontaneous creations, heredity of acquired characteristics (somatic induction) -> no feedback in genome
Darwin's thesis (1859): permanent evolution, common descent, multiplication of species, gradual change, natural selection, descending of characteristics with modification Basic conditions: too rich production of genetic variations, limitation of resources (competition) Fitness: suitability, result of multiple interactions with selection factors of the environment

6. Nature as a Standard - Evolution, Genome
Gene: functional unit, relative short segment of DNA, information how to build a protein molecule
Gene-pool: sum of all genotype-variants of a population
Genotype: all the genes (genome), generally structures, contain information, instructions to define individual characteristics
Phenotype: interpretation of the genes, expression of the genome as individual characteristics, competes with other phenotypes for reproductive success in a specific setting (basic conditions of the environment) => selection filter

7. Overview Motivation Nature as a Standard Genetic Algorithms
Classic Always Algorithm
Selection
Representation of Hypothesis
Genetic Operators
Procedure of Evolution
Schema Theorem
Applications
Genetic Programming
Evolutionary Strategies
Conclusion

8. Genetic Algorithms John H. Holland 1975 ; David E. Goldberg 1986
Goal of optimisation, "generate-and-test beam search"
Variability (Heterogenity of the characteristics, singleness, variety)
Differential fitness (propagation rate depends on the ability to survive in a specific setting, to reproduce descendants)
Heritable fitness (circulate the genome, incomplete copy, by mixture of different descendants)
Dualism Genotype/Phenotype

9. Classic Always Algorithm
Coding, structures representation of hypothesis and individuals
Interpretation function what does the coding represent?
Fitness function shall be optimised
Termination criteria is the optimum approximately reached?
Selection function which individuals determine the next population?
Initialise: generate randomly n individuals for the initial population P(0)
Evaluate: determine for all
t := 0 Generation 0
while not
Selection: choose stochastically individuals according to their fitness
Crossover: create children via the recombination of parental individuals from P'
Mutation: change randomly the representation of child individuals from P'
Update: put n, randomly picked child individuals from P' to P(t+1)
t := t + 1 increment generation
Evaluate
return Individual with highest fitness value

10. Representation of Hypothesis
Coding Representation of the parameters (hypothesis, individuals) to be optimised by structures over a discrete alphabet, mostly bit-strings s = ( ) s = (atggcaact) with alphabet A = {a, t, g, c}
Interpretation Mapping p from the genotypical structure space into the phenotypical characteristics and behaviour space
Production system s = ( ) : IF a1=T & a2=F THEN c=T ; IF a2=T THEN c=F
Triplet : amino acid

11. Selection
Best fitting individuals shall build a descendant population, one-sidedness shall be avoided by stochastic selection algorithms
Fitness-proportional selection: Roulette algorithm proportional to their own fitness, indirectly proportional to competitors. Problem: Super individuals may dominate too much
Rank-based selection: Individuals are sorted ascendingly according to their fitness; selection is done by a roulette algorithm based on the position in this ranking

12. Genetic Operators
Mutation Mutation probability, uniformly distributed random number
With a discrete alphabet and a maximal mutation distance, to limit variation. Defining the distance measure of the alphabet
P = > bit-wise cgeehadcdhh --> chdcgadcdfh Mutation distance 2 (lexicographic)

13. Genetic Operators (2)
Multipoint analogous, e.g. uniformly or odd-even: Mask:
Multi-recombination (more than 2 parental chromosomes):
random selection of 2 parents, as above
several parents \ Mask: /

14. Genetic Operators (2) Recombination (Crossover)
Crossing point(s) randomly determined or by a fixed mask
Single-point: Crossing point: Mask:
Dual-point: bbafdeacca bbabacacca Crossing points: 3, 6 edebacbfbb edefdebfbb Mask:

15. Genetic Operators (3)
Inversion mirrored (generally: permuted) insertion of the middle part > inverted fgbbcdadace --> fgbbcdcdaea permuted
Deletion loss of an arbitrary part > intercalar fgbbcdadace --> fgbbcda terminal
Duplication duplication of an arbitrary part > fgbbcdadace --> fgbbcdadacedace

16. Procedure of Evolution
Example: global maximum of a (multi-modal) function Bit-vectors: Interpretation: as in the example Evaluation: compute the function at the interpreted location
5 (3) Populations independently of each other
Strategies:
population size, recombination partner, create descendants, mutate
Plus selection from parents and mutated children
Comma selection from mutated children, individuals survive at most one generation
Variants

17. Schema Theorem
Schema: word over alphabet A*
Instance: all words which are equal to at the fixed positions
Order: o(H) number of fixed elements
Defining length: segment length between the outermost fixed positions
e.g. A = {a,b,c}, = (b, *, c, a, *, *) ; o( ) = 3 , = = 3 Instances: (b, a, c, a, a, a) , (b, b, c, a, b, c) , (b, c, c, a, c, a)
Premises: infinite large population, single-point-crossover, punctual mutation
Which templates survive (stay instances of the schema)?
exponential propagation, if
Selection: more than average fitness
Recombination: short defining length
Mutation: few fixed positions
As compact as possible conglomeration of gene groups, which are responsible for the increased fitness: building blocks

18. Applications Can be run easily in parallel
In combination with gradient algorithms (hill-climbing): Maximum search for rough restriction of the search space
Simulation of living cells
Production system as an extension to expert systems
Planning optimisation (storage, production processes, ...)
Optimal game strategies
Travelling-Salesman-Problem: structure contains indices of the nodes in visiting order. To visit each node exactly once: modification of the genetic operators
Evolution of the structure of neural nets: representation organised in segments depending on the number of output-neurones; codes the number of layers, hidden neurons and according weights

19. Overview Motivation Nature as a Standard Genetic Algorithms
Genetic Programming
Representation of the Hypothesis
Differences to GA
Applications
Evolutionary Strategies
Conclusion

20. Genetic Programming John R. Koza, 1989
Further development of the idea of genetic algorithms
Genetic creation and optimisation of computer programs for special problem-areas
Representation of the Hypothesis
Differences to GA
Applications

21. Representation of the Hypothesis
Computer program as tree structure (like parse-tree, LISP-Syntax)
Combining elements: definition of terms and functions
Arithmetic expressions: {PLUS2, MINUS2, MULT2, DIV2}
Functions: {SIN1, COS1, EXP2, LOG2, ...}
Relations, conditional statement: {LESS2, EQUAL2, IF-THEN-ELSE3, ...}
Problem related: {TURN-LEFT, PICK-UP, MOVE-RANDOM, ...}
Tree structure: IF-THEN-ELSE LESS MULT ADD A B A C B C LISP-Syntax: ( IF-THEN-ELSE ( LESS A B ) ( MULT A C ) ( ADD B C ) )
Closed under composition
Complete according to the problem to be solved

22. Differences to GA Recombination Mutation
Exchange arbitrarily chosen sub-trees
Random determination of the crossing points
Even with identical terms mostly new structure pairs
Both children survive
Mutation
Substitution of a sub-tree by a newly generated sub-tree
Random selection of a node
Substitution by a randomly new term which is correctly generated out of building blocks

23. Differences to GA Recombination, Mutation: context-sensitive variation
Selection: matches the algorithmic solution of the given problem
Formulation as fitness value, e.g.
Distance measure for numeric problems
Successfully solved / identified cases
Copy operator: copies a GP-chromosome unchanged into the next generation
Each genome is only modified by a single operator: selection between operators
Extension of terms to symbolic expressions

24. Genetic Programming (Example)24
Genetic Programming (Example)
Minimise a Boolean function

25. Genetic Programming (Example Results)
1000 individuals, 1000 steps (8 minutes)
starting length: 127, results 71, 57

26. Applications Ant searching for food on a minimal route
Classification of groups belonging together for complex areas, e.g. swallowed spirals
Robots searching for objects and doing precisely oriented moves
Robots following walls
Random number generator with a distribution as uniformly as possible
Backwards docking of a truck with its hanger
Steering a robot arm with two joints to points in a field
Design of electronic circuits for analogous filters

27. Overview Motivation Nature as a Standard Genetic Algorithms
Genetic Programming
Evolutionary Strategies
Idea, basic Principles
Differences to GA
Applications
Conclusion

28. Evolutionary Strategies
Ingo Rechenberg, 1964 / 1994
Adaptation of the basic mechanisms of natural evolution to technical optimisation problems by engineering sciences
Root: evolutionary experimental methods, focussed on the physical experiment
Results of the (at that time) unorthodox methods could not be analytically founded or reproduced
Idea, basic principles
Differences to GA
Applications

29. Development, Idea
Given: experimental equipment with variable parameters
Mechanic: changing position by pitch and angle
Elastic: outline by bending
Combination of segments of different sizes
Random change of the parameters in a certain area (mostly binomially distributed: little mutation prefered)
Measuring the experimental result: if getting worse then back propagation of the changes
Repeat until optimum is found
Representation: Parameter as real-valued vector
Original experiment: orthogonal pipe redirection with smallest loss

30. Development, Idea (2)

31. Differences to GA
Algorithmic representable, expandable to populations / several descendants
Representation expanded by strategy parameters:
Describe variance for controlling the mutation spreading of the appropriate parameter, can be integrated in the optimum search (adaptation of the increment)
Real-valued structures: adaptation of the genetic operators
Mutation: numeric deviation ; Gauss distributed random number, average 0, variance
Recombination: discrete (randomly copied from the one or the other parent chromosome), intermediary (average building), local (single individuals), global (whole population)
Random selection, no proportionality of the fitness
Surplus of descendants, selection of the best for succeeding population

32. Evolutionary Strategies (Example)
Fixed volume
Minimal surface
Fluid storage
Changeable shape

33. Evolutionary Strategies (Example Results)
100 individuals
100 generations

34. Applications
Pressure output of a two phases supersonic cone, segments with variable diameter
Flow resistance of a joint plate, 5 joints with 51 engaging levels (0, +, -) each
Rotation body form with little flow resistance, air plane ... dolphin spindle
Minimal weight construction of a bow bridge
Flexion of a lens for concentration on focus
Magic square: 30x30 with magic sum 13525
Networking with minimal lengths and a given branching degree

35. Conclusion Evolutionary algorithms solve optimisation problems
Standard is the natural evolution, which produces permanently new and partly improved organisms, which must assert themselves in their environment
Basis is the biological adaptation as a learning procedure of populations of natural organisms
Hypotheses are interpreted and evaluated by a fitness function
The hypothesis room is explored by a stochastic search: Selection as fitness proportional procedure
New hypotheses come up by recombination and mutation, similar to the chromosomes of organisms
The representation can be done by bit-strings/character-strings (GA), programs as term and function trees (GP) or real-valued parameter vectors (ES)
The convergence of the algorithms is mostly very good, but not guaranteed
They work also with complex problems, where other algorithms have failed on or are not (yet) known