1. Genetic Algorithms Genetic Programming Ata Kaban A.Kaban@cs.bham.ac.uk School of Computer Science University of Birmingham 2003

2. Genetic algorithms Genetic programming Evolutionary computation

3. Evolutionary Computation Computation procedures patterned after biological evolution Search procedures that probabilistically applies search operators to a set of points in the search space

4. The Evolutionary Cycle

5. When are useful Hard discrete optimisation problems – when the search space is very large –E.g. choosing the NN topology Very hard continuous optimisation problems Evolutionary simulations

6. Genetic algorithms (inventors) John Holland “Adaptation in Natural and Artificial Systems”, University of Michigan Press (1975)- Genetic Algorithms Lawrence Fogel, M. Evans, M. Walsh “Artificial Intelligence through Simulated Evolution”, Wiley, 1966 - Evolutionary programming Ingo Rechenburg, 1965 - Evolutionary Strategies

7. Machine Learning and Genetic Algorithms Most Machine Learning is concerned with constructing a hypothesis from examples that generalises well –fast but very biased GA is a discovery-search over hypotheses –slow and unbiased

8. The Genetic Algorithm 1. Create a population of encoded potential solutions (chromosomes) 2. Evaluate the fitness of all the chromosomes 3. Select fitter chromosomes to form new candidate population 4. Form new candidate population by recombining genes from candidate population 5. Mutate 6. Until satisfied go to 2

9. Representing hypotheses as strings (‘chromosomes’) E.g. –Outlook  {Sunny, Overcast, Rain}, –Wind  {Strong, Week} –PlayTennis  {Yes, No} Represent ‘(Overcast V Rain)  (Wind=Strong)’ by 011 10 Represent ‘IF Wind=Strong THEN PlayTennis=yes’ by 111 10 10 or 111 10 1 Can you figure out the rationale?

10. In the previous example: –Fixed length string representations for single rules –The outcome should not be constrained (11 or 00 for PlayTennis would not make sense) Designing a suitable string-based representation of hypothesis is not always as simple… Much of the success of the GA will depend on doing this is a sensitive way.

11. GA steps in more detail Operators for GA –Crossover –Mutation Selection

12. Crossover Take the new candidate solutions after selection and recombine genetic material A b c D e f a B c D e f a B c D e F A b c D e F A b c D e f a B c D e F a B c D e F A b c D e f

13. Mutation After the recombination stage, just randomly alter a few genes a B c D e F a B c D e F

14. Selection Fitness: numerical value returned by our criterion of ranking hypotheses Selection procedures: –Fitness proportionate selection –Tournament selection (size 2): 1. choose two chromosomes h 1 and h 2 at random 2. promote the fitter of the two to the next candidate population 3. until new candidate population full go to 1. Many other possibilities! Can you figure out the rationale?

15. Selection strategy We want to have some way to ensure that better individuals have a better chance of being parents then less good individuals This will give us selection pressure which will drive the population forward. We have to be careful to give less good individuals at least some chance of being parents – they may include some useful genetic material What could go wrong with the ‘Fitness proportionate selection’ procedure?

16. What could go wrong with ‘Fitness proportionate selection’? Danger of premature convergence because outstanding individuals take over the entire population very quickly Low selection pressure when fitness values are near each other

17. An example: find the maximal peak of the function (difficult optimisation)

18. Solution

19. Expected behaviour of GA Fitness proportion Fitness Generation generation

20. Genetic Programming Extension of GA for evolving computer programs (Koza 1992) –represent programs as LISP expressions –e.g. (IF (GT (x) (0)) (x) (-x)) IF GT x -x x 0

21. Another example of tree-structured program individual Need to define:  Terminals: x,y,const  Primitive functions: sin, cos, , +,-, () 2

22. Crossover on Trees A B D E G H C F I J a bc d f g e h i i e h A B D E G H C F I J a bc d f g

23. Mutation Mutation point A B D E G H C F I a bc d f g e h i

24. An early simple example: The Block problem

26. The GA solution (learned program): Trained on 166 test problems Using a population of 300 programs After 10 generations The solution found by GA which solves all 166 cases: (EQ (DU (MT CS) (NOT CS)) (DU (MS NN) (NOT NN))) What it does? Simple but it makes sense!

27. Example: Evolving programs on a mobile robot Goal: obstacle avoidance inputs from eight sensors on robot {s1-s8} with values between {0,1023} (higher values mean closer obstacle) output to two motors (speeds) m1,m2 with values between {0,15}.

28. Fitness function Penalty Reward (going fast) Reward (going straight)

29. Results

30. Electronic Filter Circuit Design Individuals are programs that transform beginning circuit to final circuit by Adding/subtracting components and connections Fitness: computed by simulating the circuit Population of 640,000 has been run on a parallel processor After 137 generations, the discovered circuits exhibited performance competitive with best human designs

31. Summary Evolutionary programming conducts randomised parallel search through the hypothesis space Approaches learning as an optimisation problem (optimise fitness) Evaluation of fitness can be very indirect Nice metaphor with Darwinian theory of biological evolution Little theoretical justification for many of the heuristics