GA tutorial.pst
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The document provides an overview of genetic algorithms, which are algorithms inspired by biological evolution that can be used to find optimal solutions to problems. The key points covered are that genetic algorithms use mechanisms like mutation, crossover, and selection to evolve solutions over multiple generations and are well-suited for large, complex search spaces. They have been successfully applied to problems in many domains like control, design, scheduling, and more.
![Considering the GA Technology
“Almost eight years ago ...
people at Microsoft wrote
a program [that] uses
some genetic things for
finding short code
sequences. Windows 2.0
and 3.2, NT, and almost
all Microsoft applications
products have shipped
with pieces of code
created by that system.”
- Nathan Myhrvold, Microsoft Advanced
Technology Group, Wired, September 1995
Wendy W illiams 27 Genetic Algorithms: A Tutorial
Metaheuristic Algorithms](https://image.slidesharecdn.com/gatutorial-pst-120928121452-phpapp01/85/GA-tutorial-pst-27-320.jpg)
GA tutorial.pst
- 1. Genetic Algorithms: A Tutorial “Genetic Algorithms are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations of things, solutions you might not otherwise find in a lifetime.” - Salvatore Mangano Computer Design, May 1995 Wendy W illiams 1 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 2. The Genetic Algorithm Directed search algorithms based on the mechanics of biological evolution Developed by John Holland, University of Michigan (1970’s) ♦ To understand the adaptive processes of natural systems ♦ To design artificial systems software that retains the robustness of natural systems Wendy W illiams 2 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 3. The Genetic Algorithm (cont.) Provide efficient, effective techniques for optimization and machine learning applications Widely-used today in business, scientific and engineering circles Wendy W illiams 3 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 4. Classes of Search Techniques S e a rc h te c h n iq u e s C a lc u lu s - b a s e d t e c h n iq u e s G u id e d ra n d o m s e a rc h te c h n iq u e s E n u m e r a t iv e t e c h n iq u e s D ir e c t m e t h o d s I n d ir e c t m e t h o d s E v o lu tio n a ry a lg o rith m s S im u la t e d a n n e a lin g D y n a m ic p r o g r a m m in g F in o n a c c i N e w to n E v o lu t io n a r y s t r a t e g ie s G e n e tic a lg o rith m s P a ra lle l S e q u e n tia l C e n tra liz e d D is trib u te d S te a d y -s ta te G e n e ra tio n a l Wendy W illiams 4 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 5. Components of a GA A problem to solve, and ... Encoding technique (gene, chromosome) Initialization procedure (creation) Evaluation function (environment) Selection of parents (reproduction) Genetic operators (mutation, recombination) Parameter settings (practice and art) Wendy W illiams 5 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 6. Simple Genetic Algorithm { initialize population; evaluate population; while TerminationCriteriaNotSatisfied { select parents for reproduction; perform recombination and mutation; evaluate population; } } Wendy W illiams 6 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 7. The GA Cycle of Reproduction children reproduction modification modified parents children population evaluation evaluated children deleted members discard Wendy W illiams 7 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 8. Population population Chromosomes could be: ♦ 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 structure ... Wendy W illiams 8 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 9. Reproduction children reproduction parents population Parents are selected at random with selection chances biased in relation to chromosome evaluations. Wendy W illiams 9 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 10. Chromosome Modification children modification modified children Modifications are stochastically triggered Operator types are: ♦ Mutation ♦ Crossover (recombination) Wendy W illiams 10 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 11. Mutation: Local Modification Before: (1 0 1 1 0 1 1 0) After: (0 1 1 0 0 1 1 0) Before: (1.38 -69.4 326.44 0.1) After: (1.38 -67.5 326.44 0.1) Causes movement in the search space (local or global) Restores lost information to the population Wendy W illiams 11 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 12. Crossover: Recombination * P1 (0 1 1 0 1 0 0 0) (0 1 0 0 1 0 0 0) C1 P2 (1 1 0 1 1 0 1 0) (1 1 1 1 1 0 1 0) C2 Crossover is a critical feature of genetic algorithms: ♦ It greatly accelerates search early in evolution of a population ♦ It leads to effective combination of schemata (subsolutions on different chromosomes) Wendy W illiams 12 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 13. Evaluation modified evaluated children children evaluation The evaluator decodes a chromosome and assigns it a fitness measure The evaluator is the only link between a classical GA and the problem it is solving Wendy W illiams 13 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 14. Deletion population discarded members discard Generational GA: entire populations replaced with each iteration Steady-state GA: a few members replaced each generation Wendy W illiams 14 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 15. An Abstract Example Distribution of Individuals in Generation 0 Distribution of Individuals in Generation N Wendy W illiams 15 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 16. A Simple Example “The Gene is by far the most sophisticated program around.” - Bill Gates, Business Week, June 27, 1994 Wendy W illiams 16 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 17. A Simple Example The Traveling Salesman Problem: Find a tour of a given set of cities so that ♦ each city is visited only once ♦ the total distance traveled is minimized Wendy W illiams 17 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 18. Representation Representation is an ordered list of city numbers known as an order-based GA. 1) London 3) Dunedin 5) Beijing 7) Tokyo 2) Venice 4) Singapore 6) Phoenix 8) Victoria CityList1 (3 5 7 2 1 6 4 8) CityList2 (2 5 7 6 8 1 3 4) Wendy W illiams 18 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 19. Crossover Crossover combines inversion and recombination: * * Parent1 (3 5 7 2 1 6 4 8) Parent2 (2 5 7 6 8 1 3 4) Child (5 8 7 2 1 6 3 4) This operator is called the Order1 crossover. Wendy W illiams 19 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 20. Mutation Mutation involves reordering of the list: * * Before: (5 8 7 2 1 6 3 4) After: (5 8 6 2 1 7 3 4) Wendy W illiams 20 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 21. TSP Example: 30 Cities 100 90 80 70 60 50 y 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 x Wendy W illiams 21 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 22. Solution i (Distance = 941) TSP30 (Performance = 941) 100 90 80 70 60 50 y 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 x Wendy W illiams 22 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 23. Solution j(Distance = 800) TSP30 (Performance = 800) 100 90 80 70 60 50 y 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 x Wendy W illiams 23 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 24. Solution k(Distance = 652) TSP30 (Performance = 652) 100 90 80 70 60 50 y 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 x Wendy W illiams 24 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 25. Best Solution (Distance = 420) TSP30 Solution (Performance = 420) 100 90 80 70 60 50 y 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 x Wendy W illiams 25 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 26. Overview of Performance TSP30 - Overview of Performance 1600 1400 1200 Distance 1000 800 600 400 200 0 Best 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 Worst Generations (1000) Average Wendy W illiams 26 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 27. Considering the GA Technology “Almost eight years ago ... people at Microsoft wrote a program [that] uses some genetic things for finding short code sequences. Windows 2.0 and 3.2, NT, and almost all Microsoft applications products have shipped with pieces of code created by that system.” - Nathan Myhrvold, Microsoft Advanced Technology Group, Wired, September 1995 Wendy W illiams 27 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 28. Issues for GA Practitioners Choosing basic implementation issues: ♦ representation ♦ population size, mutation rate, ... ♦ selection, deletion policies ♦ crossover, mutation operators Termination Criteria Performance, scalability Solution is only as good as the evaluation function (often hardest part) Wendy W illiams 28 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 29. Benefits of Genetic Algorithms Concept is easy to understand Modular, separate from application Supports multi-objective optimization Good for “noisy” environments Always an answer; answer gets better with time Inherently parallel; easily distributed Wendy W illiams 29 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 30. Benefits of Genetic Algorithms (cont.) Many ways to speed up and improve a GA-based application as knowledge about problem domain is gained Easy to exploit previous or alternate solutions Flexible building blocks for hybrid applications Substantial history and range of use Wendy W illiams 30 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 31. When to Use a GA Alternate solutions are too slow or overly complicated Need an exploratory tool to examine new approaches Problem is similar to one that has already been successfully solved by using a GA Want to hybridize with an existing solution Benefits of the GA technology meet key problem requirements Wendy W illiams 31 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 32. Some GA Application Types Domain Application Types Control gas pipeline, pole balancing, missile evasion, pursuit Design semiconductor layout, aircraft design, keyboard configuration, communication networks Scheduling manufacturing, facility scheduling, resource allocation Robotics trajectory planning Machine Learning designing neural networks, improving classification algorithms, classifier systems Signal Processing filter design Game Playing poker, checkers, prisoner’s dilemma Combinatorial set covering, travelling salesman, routing, bin packing, graph colouring and partitioning Optimization Wendy W illiams 32 Genetic Algorithms: A Tutorial Metaheuristic Algorithms
- 33. Conclusions Question: ‘If GAs are so smart, why ain’t they rich?’ Answer: ‘Genetic algorithms are rich - rich in application across a large and growing number of disciplines.’ - David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning Wendy W illiams 33 Genetic Algorithms: A Tutorial Metaheuristic Algorithms