1. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 1 Introduction to Genetic Algorithms BY PREMSANKAR.C CS S7 ROLL NO :25

2. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 2 Genetic Algorithms (GA) Overview Originally developed by John Holland (1975) A class of optimization algorithms Inspired by the biological evolution process Uses concepts of Natural Selection and Genetic Inheritance (Darwin 1859) Particularly well suited for hard problems where little is known about the underlying search space Widely-used in business, science and engineering GAs are a subclass of Evolutionary Algorithm 3. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 3 History of GAs Evolutionary computing developed in the 1960s. GAs were created by John Holland in the mid-70s. The computer model introduces simplifications (relative to the real biological mechanisms) General Introduction to GAs 4. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 4 INTRODUCTION genetic algorithms are best for searching for new solutions making use of solutions that have worked well in the past It works on large population of solutions that are repeatedly subjected to selection pressure (survival of the fittest) 5. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 5 Each solution is encoded as a chromosome (string) also called a genotype chromosome is given a measure of fitness via a fitness function. Possible information encoding Bit strings (0101 ... 1100) Real numbers (43.2 -33.1 ... 89.2) Permutations of element (E11 E3 E7 ... E1 E15) Lists of rules (R1 R2 R3 ... R22 R23) Program elements (genetic programming) 6. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 6 Classes of Search Techniques Search Techniques Calculus Base Techniques Guided random search techniques Enumerative Techniques BFSDFS Dynamic Programmin g Tabu Search Hill Climbing Simulated Anealing Evolutionary Algorithms Genetic Programming Genetic Algorithm s Fibonacci Sort 7. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 7 Genetic Algorithms vs Traditional Algorithm 1.GAs work with a coding of parameter set, not the parameter themselves. 2.GAs search from a population of points, not a single point. 3. Application of GA operators causes information from the previous generation to be carried over to the next. 4.GAs use probabilistic rules, not deterministic rules. 8. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 8 BASIC Components of a GA A problem definition as input, and Encoding principles (gene, chromosome) Initialization procedure (creation) Selection of parents (reproduction) Genetic operators (mutation, recombination) Evaluation function (environment) Termination condition 9. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 9 Initialization Start with a population of randomly generated individuals, or use - A previously saved population - A set of solutions provided by a human expert - A set of solutions provided by another heuristic algorithm 10. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 10 ENCODING Each chromosome has one binary string. Each bit in this string can represent some characteristic of the solution. The binary string of chromosome example 11. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 11 FITTNESS FUNCTION Determine the fitness of each member of the population Perform the objective function on each population member . FitnessScaling adjusts down the fitness values of the super- performers and adjusts up the lower performers. 12. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 12 Genetic Operators Three major operations of genetic algorithm are Selection replicates the most successful solutions found in a population Recombination decomposes two distinct solutions and then randomly mixes their parts to form new solutions Mutation randomly changes a candidate solution(0-1) 13. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 13 MUTATIONMUTATION Purpose: to simulate the effect of errors that happen with low probability during duplication For binary encoding we can switch randomly chosen bits from 1 to 0 or from 0 to 1. Results: - Movement in the search space - Restoration of lost information to the population 14. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 14 SELECTION(reproduction) Purpose: to focus the search in promising regions of the space Inspiration: Darwins survival of the fittest . Example: the probability of selecting a string with a fitness value of f is f/ft, ft is the sum of all of the fitness values in the population 15. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 15 CROSSOVERCROSSOVER (Recombination ) A. One-point crossover B. Two-point crossover Crossover selects genes from parent chromosomes and creates a new one choose some crossover point everything before this point copies from the first parent and then everything after the crossover copies from the second parent Causes an exchange of genetic material between two parents 16. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 16 Single Point Crossover Example Parent 1 1 0 0 $ 1 0 0 1 0 1 0 Parent 2 0 0 1 $ 0 1 1 0 1 1 1 Child 1 1 0 0 $ 0 1 1 0 1 1 1 Child 2 0 0 1 $ 1 0 0 1 0 1 0 Double Point Crossover Example Parent 1 1 1 0 1 0 0 $ 1 0 0 1 $ 0 1 1 Parent 2 0 1 0 1 1 0 $ 0 0 1 0 $ 1 0 1 Child 1 1 1 0 1 0 0 $ 0 0 1 0 $ 0 1 1 Child 2 0 1 0 1 1 0 $ 1 0 0 1 $ 1 0 1 17. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 17 Termination condition A solution is found that satisfies minimum criteria Fixed number of generations reached Allocated budget (computation time/money) reached The highest ranking solution's fitness is reaching A satisfactory solution has been achieved No improvement in solution quality 18. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 18 SIMPLE GENETIC ALGORITHM 1. Create a Random Initial State 2. Evaluate Fitness 3. Crossover ( recombination) 4. Reproduce 5. Repeat until successful. 6. Terminate 19. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 19 THE EVOLUTIONARY CYCLE selection population evaluation modification discard deleted members parents modified members evaluated initiate evaluate 20. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 20 Initial Population Selection Reproduction Mutation Next Iteration (Generation) Block diagram 21. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 21 Recombination (cross-over) can when using bitstrings schematically be represented: Using a specific cross-over point 1 0 0 1 1 0 1 0 1 0 1 1 1 0 X 1 0 0 1 1 1 0 0 1 0 1 1 0 1 22. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 22 Mutation prevents the algorithm to be trapped in a local minimum In the bitstring approach mutation is simpy the changing of one of the bits 1 0 0 1 1 0 1 1 1 0 1 1 0 1 23. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 23 ADVANTAGES OF GENETIC ALGORITHMS A fastest search technique GAs will produce "close" to optimal results in a "reasonable" amount of time Suitable for parallel processing Fairly simple to develop Makes no assumptions about the problem space 24. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 24 DRAWBACKS Number of permutations of functions and variables. The search space is vast. Most GPs are limited in the available operators and terminals they can use. It requires a lot of computer work, even when a good set of operations, terminals and controlling algorithm are chosen 25. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 25 APPLICATIONS OF GENETIC ALGORITHMS genetic programming Scheduling: Facility, Production, Job, and Transportation Scheduling Design: Circuit board layout, Communication Network design, keyboard layout, Parametric design in aircraft Machine Learning: Designing Neural Networks, Classifier Systems, Learning rules Image Processing: Pattern recognition 26. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 26 CONCLUSION GAs are a powerful tool for global search GA are best for searching for new solutions and making use of solutions that have worked well in the past 27. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 27 ANY OUESTIONS ? 28. Toc-H INSTITUTE OF SCIENCE & TECHNOLOGY 28