Genetic Algorithms

1. Genetic Algorithms By: Jacob Noyes 4/16/2013

2. Traveling Salesman Problem • Given: • A list of cities • Distances between each city • Find: • Shortest path to reach every city once

3. Traveling Salesman Problem • Brute force • Exact Algorithms • Pro: • Will find the right answer • Con: • Does not scale well

4. Evolution • Change in inherited characteristics over time • Natural Selection: • A mechanism through which evolution happens • Survival of the fittest • Genes of the more suitable organisms get passed on more • often • Deoxyribonucleic acid(DNA): • Genes encoded in amino acids • Used to pass genes from parent to offspring

5. Genetic Algorithms: The Basics • Genetic algorithms are specialized search • heuristics which use the fundamental • principles of evolution through natural • selection to find the best solution to a • problem.

6. Genetic Algorithms: The Basics • Encoding • Initialization • Selection • Crossover • Mutation

7. Encoding • Changeable representation of individual's traits • is created • Completed only at the start • Its “string” is designed a series of bits • Concatenate multiple parameters

8. Examples: Max y-values? • Example 1: • y = -x^2 + 255x • 0 ≤ x ≤ 255 • String: 00000000 ≤ x ≤ 11111111 • Example 2: • y = 2w + x + 3z • 0 ≤ w ≤ 7, 0 ≤ x ≤ 7, 0 ≤ z ≤ 7 • String: 000/000/000 – 111/111/111

9. Initialization • Beginning population is created • Each bit(gene) is randomly generated to create variety • Performed only for the first generation and not repeated

10. Example: Initialization • Given • y = -x^2 + 255x • 0 ≤ x ≤ 255 X Binary x 188 10111100 48 00110000 75 01001011 104 01101000 249 11111001 10 00001010 134 10000110 125 01111101

11. Selection • Assign a fitness • measure of how close a solution is to fulfilling the problem • Assigned to each individual • Select individuals • Individuals with higher fitness will reproduce more often • Non-selected individuals will “die off”

12. Example: Fitness • Given • y = -x^2 + 255x • 0 ≤ x ≤ 255 X Binary x Fitness 188 10111100 12596 48 00110000 9936 75 01001011 13500 104 01101000 15704 249 11111001 1494 10 00001010 2450 134 10000110 16214 125 01111101 16250

13. Optimums • Local optimum: A point where small • changes will lead to worse • results • Overall optimum: The best solution

14. Selection: Categories • Proportionate Selection: Fitness relative to other • individuals • Ranking Selection: Chance to reproduce based • on order • Tournament Selection: Pits individuals against • each other in smaller brackets • Gender Specific Selection: Splits Individuals • into groups based on “sex” • Genetic Relatedness Based Selection: • Individuals are selected based on their • genetic distance from others in the population

15. Selection: Proportionate Selection • Roulette wheel selection • Deterministic Sampling • Stochastic Remainder Sampling with Replacement • Stochastic Remainder Sampling without Replacement • Stochastic Universal Selection

16. Roulette Wheel Selection • 1. Find Pf: Population fitness = sum of all fitness factors • 2. Find Psel: Each individual's probability of selection • Psel = (fitness factor) / Pf • 3. Load each Psel into an array • 4. Generate random number between 0-100 • 5. Start at beginning of array, subtract each Psel from number until number <= 0

17. Deterministic Sampling • 1. Average fitness is found • 2. Individual fitnesses are divided by the average • 3. Whole number results = number of spots in the mating pool • 4. Extra slots filled starting by highest decimal • 5. Random numbers generated to select individuals from the mating pool

18. Stochastic Remainder with Replacement • Uses Deterministic Sampling to fill slots with whole number results • Left over slots are then filled using the remainders with the Roulette Wheel • Selection Method

19. Stochastic Remainder without Replacement • Uses Deterministic Sampling to fill slots with whole number results • Uses a “weighted-coin toss” to determine the rest • 1. Each remainder multiplied by 100 • 2. Random number between 0-100 generated • 3. If random number <= remainder, accept • 4. Loop until all spots are filled

20. Ranking Selection • Chance to breed based on order of fitness, not proportion • Pro: Easy to implement and understand • Con: Generally less accurate, less efficient, and • phase out diversity too quickly • Due to cons, not used often • Types: • Linear ranking selection • Truncate Selection

21. Linear Ranking Selection • 1. Probabilities are set up for each rank • before fitnesses are even assessed • 2. Individuals are ordered based on fitness • level • 3. The predefined probabilities are • assigned to their rank • 4. Individuals are selected based on the • probabilities

22. Truncate Selection • 1. Candidates are put in order based on fitness • 2. The top predefined percentage are chosen to reproduce

23. Tournament Selection • Individuals are pitted against each other in smaller brackets • The winner(s) of each bracket reproduces • Bracket participants only need to know fitness levels of others in bracket • No need for total or average population fitness factors • Good for situations when it is impossible or implausible to • calculate totals

24. Tournament Selection: Categories • Binary Tournament Selection • Larger Tournament Selection • Boltzmann Tournament Selection • Correlative Tournament Selection

25. Binary Tournament Selection • 1. Two candidates are randomly selected out of • possible solutions • 2. Candidate with best fitness factor is chosen • to reproduce

26. Larger Tournament Selection • 1. More than two candidates are randomly selected out • of possible solutions • 2. Candidate with best fitness factor is chosen to • reproduce • Only difference from Binary Tournament Selection is • number of candidates in each bracket • More candidates = higher selection pressure

27. Boltzmann Tournament Selection • N = temperature = variable describing number of differences in bit string • between two individuals • 1. First candidate is chosen randomly • 2. Second candidate is chosen as having exactly n differences in gene • string from first candidate • 3. Third candidate is chosen • Half of the time has exactly n differences in gene string from first AND • second candidate (strict choice) • Other half of the time has exactly n difference in gene string from ONLY • first candidate (relaxed choice) • 4. Choose the winner of the three to reproduce

28. Correlative Tournament Selection • Not so much a separate selection method as much as an extension of other • tournament selections • Once mating pool is selected, pairs are created based on how closely they • are related • Pairing similar individuals allows a better chance of passing on their • (probably) good similar trait

29. Gender Specific Selection • Genetic Algorithm with Chromosome Differentiation(GACD) • Restricted Mating • Correlative Family-based Selection

30. Genetic Algorithms with Chromosome Differentiation • Every individual has an extra 00 or 01 attached to their bit string • 00 = female, 01 = male • When a male and female mate each parent randomly selects a bit to pass • onto the child • Females(00) can pass on 0 or 0 • Males(01) can pass on 0 or 1 • Hamming distance: the sum of the differences between each bit of two individuals • Ex: 00011111 and 11111111 have a hamming distance of 3.

31. Genetic Algorithm With Chromosome Differentiation • 1. Males generated first randomly • 2. Females created for each male with maximum hamming distance • 3. Select individuals to put into mating pool by either: • Using a separate selection method for each sex • Or, lumping them together and using one selection method over all of them • 4. Mate each individual in the mating pool twice • 5. If there are fewer of one sex in the mating pool, mate leftovers with the • highest fitness individual of the opposite sex

32. Restricted Mating • In nature, different species cannot or will not mate • Restricted mating is based on species differentiations • Certain traits (predefined sections of the bit string) must be the same to • mate two candidates • Keeps several variations from converging to a local optimum

33. Correlative Family-based Selection • 1. Two candidates are mated together twice • 2. Between the two candidates and the two children, the most fit • solution is chosen • 3. The hamming distance is calculated for each individual compared • to the other three • 4. The individual with the highest hamming distance is also chosen • to reproduce

34. Genetic Relatedness Based Selection • Purpose is to search unexplored areas of the search space • Groups candidates based on similar fitness factors • Does not try to find most fit candidates • Includes: • Fitness Uniform Selection Scheme(FUSS) • Reserve Selection

35. Fitness Uniform Selection Scheme • Candidates with similar fitness factors are grouped together • Random numbers are generated from the range of minimum fitness to • maximum fitness • Candidates with fitnesses closest to the random number are selected • This gives a higher probability of selecting unexplored areas • Helps avoid local optimums

36. Reserve Selection • Candidates split into two categories • Non-reserved: Normal candidates with normal selection process applied • Reserved: Specific less fit candidates that are carried over from generation • to generation to keep variety in the population • Keeps pool out of local maximums

37. Elitism • Automatically carry over most fit individual to next generation • Extension of other selection methods • Makes sure best fit does not just get unlucky

38. Example: Selection • Given • y = -x^2 + 255x • 0 ≤ x ≤ 255 • Top half truncate selection X Binary x Fitness 188 10111100 12596 48 00110000 9936 75 01001011 13500 104 01101000 15704 249 11111001 1494 10 00001010 2450 134 10000110 16214 125 01111101 16250 Gene pool 01111101 10000110 01101000 01001011

39. Crossover • Genes(bit strings) are combined from both parents to create offspring • Locus: the randomly generated point(s) at which each parent's bit string • is separated

40. Example: Crossover Candidate Gene pool Locus 1 01111101 3 2 10000110 1 3 01101000 1 4 01001011 6 • Parents P1 String P2 String Offspring • 1, 2 0110011001100110 • 2, 1 1001110110011101 • 2, 3 1110100011101000 • 3, 2 0000011000000110 • 3, 4 0100101101001011 • 4, 3 0110100001101000 • 4, 1 010010 01 01001001 • 1, 4 011111 11 01111111

41. Mutation in The Natural World • Brings diversity to a population • Without mutation, just different combinations of the same traits • Mutations happen when DNA is not copied properly • If the mutation has a benefit, or is just not a hindrance, it may be passed • on to new generations

42. Mutation in Genetic Algorithms • Purposely inject after crossover • Rate of mutation is decided beforehand • Ex: 1/2000th chance of mutation per bit • For every bit in a population, a random number is generated • If the probability hits, the bit is XOR'ed with 1

43. Example: Mutation • Given • mutation rate: 1/64 • Pre-mutated XOR Offspring • 01100110 00000000 01100110 • 10011101 00000000 10011101 • 11101000 00000010 11101010 • 00000110 00000000 00000110 • 01001011 00000000 01001011 • 01101000 00000000 01101000 • 01001001 00000000 01001001 • 01111111 00000000 01111111

44. Genetic Algorithms: End • Fitness threshold based • Each solution's fitness level is checked after each generation • If a given minimum fitness level is achieved, the algorithm finishes • running and outputs the maximum fitness candidate • Generation threshold based • Genetic algorithm runs for a predefined number of generations • Most fit solution over all generations is outputted

45. Uses of Genetic Algorithms • Optimal water network layouts • Facial recognition • Robotics • Trajectories for spacecraft • Fun with walking • Much More

46. Questions?