1. Introduction to Evolutionary Computing II A.E. Eiben Free University Amsterdam http://www.cs.vu.nl/~gusz/ with thanks to the EvoNet Training Committee and its “Flying Circus”

2. A.E. Eiben, Introduction to EC II2 EvoNet Summer School 2002 Contents The evolutionary mechanism and its components Examples: the 8-queens problem Working of an evolutionary algorithm EC dialects and beyond Advantages & disadvantages of EC Summary

3. A.E. Eiben, Introduction to EC II3 EvoNet Summer School 2002 The main evolutionary cycle Population Parents Parent selection Survivor selection Offspring Recombination (crossover) Mutation Intialization Termination

4. A.E. Eiben, Introduction to EC II4 EvoNet Summer School 2002 The two pillars of evolution Increasing population diversity by genetic operators mutation recombination Push towards novelty Decreasing population diversity by selection of parents of survivors Push towards quality There are two competing forces active

5. A.E. Eiben, Introduction to EC II5 EvoNet Summer School 2002 Components: representation / individuals (1) Individuals have two levels of existence phenotype: object in original problem context, the outside genotype: code to denote that object, the inside (a.k.a. chromosome, “digital DNA”): a d c a a c b genotype: phenotype: The link between these levels is called representation

6. A.E. Eiben, Introduction to EC II6 EvoNet Summer School 2002 Genotype spacePhenotype space Encoding (representation) Decoding (inverse representation) B 0 c 0 1 c d G 0 c 0 1 c d R 0 c 0 1 c d Components: representation / individiuals (2)

7. A.E. Eiben, Introduction to EC II7 EvoNet Summer School 2002 Sometimes producing the phenotype from the genotype is a simple and obvious process. Other times the genotype might be a set of parameters to some algorithm, which works on the problem data to produce the phenotype Problem Data Genotype Phenotype Growth Function Components: representation / individuals (3)

8. A.E. Eiben, Introduction to EC II8 EvoNet Summer School 2002 Components: representation / individuals (4) Search takes place in the genotype space Evaluation takes place in the phenotype space Repr: Phenotypes  Genotypes Fitness(g) = Value(repr -1 (g)) Repr must be invertible, in other words decoding must be injective (Q: surjective?) Role of representation: defines objects that can be manipulated by (genetic) operators Note back on Darwinism: no mutations on phenotypic level! (right term: small random variations)

9. A.E. Eiben, Introduction to EC II9 EvoNet Summer School 2002 Components: evaluation, fitness measure Role: represents the task to solve, the requirements to adapt to enables selection (provides basis for comparison) Some phenotypic traits are advantageous, desirable, e.g. big ears cool better, These traits are rewarded by more offspring that will expectedly carry the same trait

10. A.E. Eiben, Introduction to EC II10 EvoNet Summer School 2002 Components: population Role: holds the candidate solutions of the problem as individuals (genotypes) Formally, a population is a multiset of individuals, i.e. repetitions are possible Population is the basic unit of evolution, i.e., the population is evolving, not the individuals Selection operators act on population level Variation operators act on individual level

11. A.E. Eiben, Introduction to EC II11 EvoNet Summer School 2002 Components: selection Role: Gives better individuals a higher chance of becoming parents surviving Pushes population towards higher fitness E.g. roulette wheel selection fitness(A) = 3 fitness(B) = 1 fitness(C) = 2 A C 1/6 = 17% 3/6 = 50% B 2/6 = 33%

12. A.E. Eiben, Introduction to EC II12 EvoNet Summer School 2002 Components: Mutation Role: causes small (random) variance before 1 1 1 0 1 1 1 after 1 1 1 1 1 1 1

13. A.E. Eiben, Introduction to EC II13 EvoNet Summer School 2002 Components: Recombination 1 1 1 1 1 1 10 0 0 0 0 0 0 parents cut 1 1 1 0 0 0 00 0 0 1 1 1 1 offspring Role: combines features from different sources

14. A.E. Eiben, Introduction to EC II14 EvoNet Summer School 2002 Place 8 queens on an 8x8 chessboard in such a way that they cannot check each other Example: the 8 queens problem

15. A.E. Eiben, Introduction to EC II15 EvoNet Summer School 2002 The 8 queens problem Representation 12345678 Genotype: a permutation of the numbers 1 - 8 Phenotype: a board configuration Obvious mapping

16. A.E. Eiben, Introduction to EC II16 EvoNet Summer School 2002 Penalty of one queen: the number of queens she can check. Penalty of a configuration: the sum of the penalties of all queens. Note: penalty is to be minimized Fitness of a configuration: inverse penalty to be maximized The 8 queens problem Fitness evaluation

17. A.E. Eiben, Introduction to EC II17 EvoNet Summer School 2002 The 8 queens problem Mutation Small variation in one permutation, e.g.: swapping values of two randomly chosen positions, or inverting a randomly chosen segment 12345678 12345678

18. A.E. Eiben, Introduction to EC II18 EvoNet Summer School 2002 The 8 queens problem Recombination Combining two permutations into two new permutations: choose random crossover point copy first parts into children create second part by inserting values from other parent: in the order they appear there beginning after crossover point skipping values already in child 87 64253 1 1352467 8 87 6451 2 3 1356287 4

19. A.E. Eiben, Introduction to EC II19 EvoNet Summer School 2002 Parent selection: Roulette wheel selection, for instance Survivor selection (replacement) When inserting a new child into the population, choose an existing member to replace by: sorting the whole population by decreasing fitness enumerating this list from high to low replacing the first with a fitness lower than the given child Note: selection works on fitness values, no need to adjust it to representation The 8 queens problem Selection

20. A.E. Eiben, Introduction to EC II20 EvoNet Summer School 2002 Working of an EA Phases in optimizing on a 1-dimensional fitness landscape Early phase: quasi-random population distribution Mid-phase: population arranged around/on hills Late phase: population concentrated on high hills

21. A.E. Eiben, Introduction to EC II21 EvoNet Summer School 2002 Typical run Typical run of an EA shows so-called “anytime behavior” Best fitness in population Time (number of generations)

22. A.E. Eiben, Introduction to EC II22 EvoNet Summer School 2002 Best fitness in population Time (number of generations) Progress in 1 st half Progress in 2 nd half Long runs?

23. A.E. Eiben, Introduction to EC II23 EvoNet Summer School 2002 Time (number of generations) Best fitness in population T: time needed to reach level F after random initialisation T F: fitness after smart initialisation F Smart initialisation?

24. A.E. Eiben, Introduction to EC II24 EvoNet Summer School 2002 Scale of “all” problems Performance of methods on problems Random search Special, problem tailored method Evolutionary algorithm Goldberg’89 view

25. A.E. Eiben, Introduction to EC II25 EvoNet Summer School 2002 EAs and domain knowledge Trend in the 90ies: adding problem specific knowledge to EAs (special variation operators, repair, etc) Result: EA performance curve “deformation”: better on problems of the given type worse on problems different from given type Amount of added knowledge is variable

26. A.E. Eiben, Introduction to EC II26 EvoNet Summer School 2002 Performance of methods on problems EA 1EA 4 EA 3 EA 2 Scale of “all” problems P Michalewicz’96 view

27. A.E. Eiben, Introduction to EC II27 EvoNet Summer School 2002 General EA framework and dialects There is a general, formal EA framework (omitted here) In theory: every EA is an instantiation of this framework, thus: specifying a particular EA or a type of EAs (a “dialect”) needs only filling in the characteristic features In practice this would be too formalistic there are many exceptions (EAs not fitting into this framework) why care about the taxonomy, or label?

28. A.E. Eiben, Introduction to EC II28 EvoNet Summer School 2002 Genetic algorithms & genetic programming Genetic algorithms (USA, 70’s, Holland, DeJong): Typically applied to: discrete optimization Attributed features: not too fast good solver for combinatorial problems Special: many variants, e.g., reproduction models, operators Genetic programming (USA, 90’s, Koza) Typically applied to: machine learning tasks Attributed features: competes with neural nets and alike slow needs huge populations (thousands) Special: non-linear chromosomes: trees, graphs

29. A.E. Eiben, Introduction to EC II29 EvoNet Summer School 2002 Evolution strategies & evolutionary programming Evolution strategies (Germany, 70’s, Rechenberg, Schwefel) Typically applied to: numerical optimization Attributed features: fast & good optimizer for real-valued optimization relatively much theory Special: self-adaptation of (mutation) parameters standard Evolutionary programming (USA, 60’s, Fogel et al.) Typically applied to: machine learning (old EP), optimization Attributed features: very open framework: any representation and mutation op’s OK Special: no recombination self-adaptation of parameters standard (contemporary EP)

30. A.E. Eiben, Introduction to EC II30 EvoNet Summer School 2002 Beyond dialects Field merging from the early 1990’s No hard barriers between dialects, many hybrids, outliers Choice for dialect should be motivated by given problem Best practical approach: choose representation, operators, population model, etc. pragmatically (and end up with an “unclassifiable” EA) There are general issues for EC as a whole

31. A.E. Eiben, Introduction to EC II31 EvoNet Summer School 2002 Advantages of EC No presumptions w.r.t. problem space Widely applicable Low development & application costs Easy to incorporate other methods Solutions are interpretable (unlike NN) Can be run interactively, accommodate user proposed solutions Provides many alternative solutions Intrinsic parallelism,straightforward parallel implementations

32. A.E. Eiben, Introduction to EC II32 EvoNet Summer School 2002 Disadvantages of EC No guarantee for optimal solution within finite time Weak theoretical basis May need parameter tuning Often computationally expensive, i.e. slow

33. A.E. Eiben, Introduction to EC II33 EvoNet Summer School 2002 The performance of EC Acceptable performance at acceptable costs on a wide range of problems EC niche (where supposedly superior to other techniques): complex problems with one or more of the following features many free parameters complex relationships between parameters mixed types of parameters (integer, real) many local optima multiple objectives noisy data changing conditions (dynamic fitness landscape)

34. A.E. Eiben, Introduction to EC II34 EvoNet Summer School 2002 Summary Evolutionary Computation: is a method, based on biological metaphors, of breeding solutions to problems has been shown to be useful in a number of areas could be useful for your problem its easy to give it a try is FUN