CS4413 Algorithmic Processes Genetic Algorithms (These materials are used in the classroom only)

1. CS4413 Algorithmic Processes Genetic Algorithms (These materials are used in the classroom only)

2. Genetic Algorithms (GA) OVERVIEW • A class of probabilistic optimization algorithms. • Inspired by the biological evolution process. • Uses concepts of “Natural Selection” and “Genetic Inheritance” (Darwin 1859) • Originally developed by John Holland (1975)

3. GA overview (cont) • Particularly well suited for hard problems where little is known about the underlying search space. • Widely-used in business, science and engineering.

4. Classes of Search Techniques Search Techniques Calculus Base Techniques Enumerative Techniques Guided random search techniques Sort Dynamic Programming Fibonacci DFS BFS Evolutionary Algorithms Tabu Search Hill Climbing Simulated Anealing Genetic Programming Genetic Algorithms

5. Differences from Traditional Methods • Work with a coding of the parameter set. • Not the parameters itself • Search from population of points. • Use probabilistic transition rules. • Use objective functions • Not derivatives. • Other auxiliary knowledge.

6. A genetic algorithm maintains apopulation ofcandidate solutionsfortheproblemat hand, and makes itevolve byiteratively applying a set of stochastic operators … Formal Definition

7. Stochastic operators • REPRODUCTION replicates the most successful solutions found in a population at a rate proportional to their relative quality. • CROSSOVER decomposes two distinct solutions and then randomly mixes their parts to form novel solutions. • MUTATION randomly perturbs a candidate solution.

8. The Metaphor

9. The Metaphor (cont)

10. Components of a GA • A problem definition as input, • Encoding principles (gene, chromosome) • Initialization procedure (creation) • Selection of parents (reproduction) • Genetic operators (crossover, mutation) • Evaluation function (environment) • Termination condition (to get final solutions)

11. Representation (encoding) • Possible individual’s encoding • Bit strings (0101 ... 1100) • Real numbers (43.2 -33.1 ... 0.0 89.2) • Permutations of element (E11 E3 E7 ... E1 E15) • Lists of rules (R1 R2 R3 ... R22 R23) • Program elements (genetic programming) • ... any data structures ...

12. Representation (cont) When choosing an encoding method rely on the following key ideas • Use a data structure as close as possible to the natural representation. • Write appropriate genetic operators as needed. • If possible, ensure that all genotypes correspond to feasible solutions. • If possible, ensure that genetic operators preserve feasibility.

13. Initialization • Start with a population of randomly generated individuals, or use • Previously saved population. • A set of solutions provided by a human expert. • A set of solutions provided by another heuristic algorithm.

14. Reproduction • Purpose: to focus the search in promising regions of the space. • Inspiration: Darwin’s “survival of the fittest” • Trade-off between exploration and exploitation of the search space. • There are different types …

15. Fitness Proportionate Reproduction • Derived by Holland as the optimal trade-off between exploration and exploitation Drawbacks • Different selection for f1(x) and f2(x) = f1(x) + c • Super individuals cause convergence (that may be premature).

16. Linear Ranking Reproduction Based on sorting of individuals by decreasing fitness The probability to be extracted for the ith individual in the ranking is defined as whereb can be interpreted as the expected sampling rate of the best individual

17. Local Tournament Reproduction Extracts kindividuals from the population with uniform probability (without re-insertion) and makes them play a “tournament”,where the probability for an individual to win is generally proportional to its fitness Reproduction pressure is directly proportional to the number kof participants.

18. Recombination (Crossover) • Enables the evolutionary process to move toward promising regions of the search space. • Matches good parents’ sub-solutions to construct better offspring.

19. Mutation • Purpose: to simulate the effect of errors that happen with low probability during duplication. Results • Movement in the search space • Restoration of lost information to the population

20. Evaluation (fitness function) • Solution is only as good as the evaluation function; choosing a good one is often the hardest part. • Similar-encoded solutions should have a similar fitness.

21. Termination condition • A pre-determined number of generations or time has elapsed. • A satisfactory solution has been achieved. • No improvement in solution quality has taken place for a pre-determined number of generations.

22. Simple Genetic Algorithms produce an initial population of individuals evaluate the fitness of all individuals while termination condition not met do select fitter individuals for reproduction recombine between individuals for crossover mutate individuals evaluate the fitness of the modified individuals generate a new population End while

23. The Evolutionary Cycle parents selection modification modified offspring initiate & evaluation population evaluate evaluated offspring deleted members discard

24. Example:the MAXONE problem • Suppose we want to maximize the number of ones in a string of l binary digits Is it a trivial problem? • It may seem so because we know the answer in advance. • However, we can think of it as maximizing the number of correct answers, each encoded by 1, to l yes/no difficult questions…

25. Example (cont) • An individual is encoded (naturally) as a string of l, length, binary digits. • The fitness f of a candidate solution to the MAXONE problem is the number of ones in its genetic code. • We start with a population of n random strings. Suppose that l = 10 and n = 6.

26. Example (initialization) We toss a fair coin 60 times and get the following initial population: s1 = 1111010101f (s1) = 7 s2 = 0111000101f (s2) = 5 s3 = 1110110101f (s3) = 7 s4 = 0100010011f (s4) = 4 s5 = 1110111101 f (s5) = 8 s6 = 0100110000f (s6) = 3

27. Individual i will havea probability to be chosen Example (selection1) Next we apply fitness proportionate selection with the roulette wheel method: We repeat the extraction as many times as the number of individuals we need to have the same parent population size (6 in our case) Area is Proportional to fitness value 1 2 n 3 4

28. Example (Reproduction2) Suppose that, after performing selection, we get the following population: s1` = 1111010101(s1) s2` = 1110110101(s3) s3` = 1110111101(s5) s4` = 0111000101(s2) s5` = 0100010011(s4) s6` = 1110111101(s5)

29. Example (Crossover1) Next we mate strings for crossover. For each couple we decide according to crossover probability (for instance 0.6) whether to actually perform crossover or not. Suppose that we decide to actually perform crossover only for couples (s1`, s2`) and (s5`, s6`). For each couple, we randomly extract a crossover point, for instance 2 for the first and 5 for the second.

30. Before crossover: s1` = 1111010101 s2` = 1110110101 s5` = 0100010011 s6` = 1110111101 After crossover: s1`` = 1110110101 s2`` = 1111010101 s5`` = 0100011101 s6`` = 1110110011 Example (Crossover2)

31. Example (Mutation1) The final step is to apply random mutation: for each bit that we are to copy to the new population we allow a small probability of error (for instance 0.1) Before applying mutation: s1`` = 1110110101 s2`` = 1111010101 s3`` = 1110111101 s4`` = 0111000101 s5`` = 0100011101 s6`` = 1110110011

32. Example (mutation2) After applying mutation: s1``` = 1110100101 f (s1``` ) = 6 s2``` = 1111110100f (s2``` ) = 7 s3``` = 1110101111 f (s3``` ) = 8 s4``` = 0111000101 f (s4``` ) = 5 s5``` = 0100011101 f (s5``` ) = 5 s6``` = 1110110001 f (s6``` ) = 6

33. Example (end) In one generation, the total population fitness changed from 34 to 37, thus improved by ~9%. At this point, we go through the same process all over again, until a stopping criterion is met.

34. Another Example-Our Research Project • GAC: Genetic Based Call Admission Control (GAC) Scheme for Next Generation Wireless Systems.

35. Call Admission Control • Call Admission Control (CAC) Scheme • CAC deals with the question whether or not a wireless controller can accept new connection for a mobile user based on; • Available bandwidth • QoS requirements for both sides • Other performance issues Different CAC Schemes are used in order to provide more efficient CAC in wireless systems…

36. Next Generation Wireless Systems Next Generation Wireless Systems (NGWS) • Convergence of heterogeneous wireless systems • WLAN, 2G/3G Systems, Satellite Networks • IP-based Infrastructure • Anytime/anywhere high rate data & multimedia

37. Motivation • Heterogeneous Service Demands Efficient Handoff Management: • High network utilization. • Minimum cost and handoff latency. • Mobile terminal QoS requirements. • Unified call admission control (CAC) needed • Address the architectural heterogeneities • Achieve the best performance. • Fast and accurate manner. • Low Handoff latency. • Fast and Intelligent algorithms are an efficient way of solving this problem.

38. Motivation … • Genetic Algorithms (GA) in Handoff Management: • GA for inter-system handoff. • Find the optimum set of resources in a fast manner. • Architecture • Coverage area • Resource amount • QoS parameters, etc. • Fast and Accurate optimization. • Decrease • Handoff latency • Cost

39. Solution Approach for GAC • Establish continuous time Markov Decision Process (CTMDP) model. • Define Optimal Decision Policy for handoff. • Expected cost for any wireless network architecture considering each network resources • State Transition Probability • Deploy Genetic Algorithms (GAs) with derived optimality equation.

40. Dynamic factors and different constraints (b/w, latency, network load, power, capacity) are considered in hand-off decision. Set of statesDifferent wireless network architectures, N Cost Function Set of actionsHandoff decision by binary variablea Є A = {0,1} Theory • Sg: Signaling to set-up handoff • Sw: Switching of the traffic during handoff • Pw: Power consumption of the network • Bw: Bandwidth of the network

41. Theory … • State Transition Probability • Optimality Equations

42. Methodology • Global search method simulating natural evolution and genetics. • Using nature and survival of the fittest techniques and parallel processing. • The algorithm; • Generate an initial population • Evaluate fitness • Select best chromosomes • Perform crossover • Perform mutation • Return ii.

43. GAC Protocol Operation GA Operational Flow Chart

44. Performance Evaluation NX:Different wireless network architectures within the mobile terminal’s roaming AvBW:Available bandwidth capacity at the time instant

45. Performance Evaluation CAC latency experienced with GAC scheme and Heuristic algorithm for varying handoff scenarios.

46. Performance Evaluation CAC latency experienced with GAC scheme and Heuristic algorithm for varying handoff scenarios and different crossover value, Xover=0.65.

47. Performance Evaluation CAC latency experienced with GAC scheme and Heuristic algorithm for varying handoff scenarios and different mutation value, Mut=0.0005.

48. Performance Evaluation Handoff cost experienced with GAC scheme and other CAC schemes for varying handoff scenarios.

49. Performance Evaluation Call admission percentage comparison for varying handoff scenarios.