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CS26110 AI Toolbox. Evolutionary and Genetic Algorithms 2. GA Megaman controller http://www.youtube.com/watch?v=c7xJNAJys2s Procedural animation via GAs http://www.youtube.com/watch?v=_v9_hTSOHN4 Starcraft 2 build orders
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CS26110AI Toolbox Evolutionary and Genetic Algorithms 2
GA Megaman controller • http://www.youtube.com/watch?v=c7xJNAJys2s • Procedural animation via GAs • http://www.youtube.com/watch?v=_v9_hTSOHN4 • Starcraft 2 build orders • http://lbrandy.com/blog/2010/11/using-genetic-algorithms-to-find-starcraft-2-build-orders/ • Evolving locomotion • http://www.youtube.com/watch?v=STkfUZtR-Vs
Basic ideas of EAs • An EA is an iterative procedure which evolves a population of individuals • Each individual is a candidate solution to a given problem • Each individual is evaluated by a fitness function, which measures the quality of its candidate solution • At each iteration (generation): • The best individuals are selected • Genetic operators are applied to selected individuals in order to produce new individuals (offspring) • New individuals are evaluated by fitness function
Representation CHROMOSOME GENE 0.12 1 A 0.33 0 C B 1 5.2 0 -1.2 C W 3.0 0 1.18 0 W 1 20.4 Q S 1 5.2
Task • The knapsack problem is as follows: • given a set of weights W, and a target weight T, find a subset of W whose sum is as close to T as possible • Example: W = {5, 8, 10, 23, 27, 31, 37, 41} and T = 82 • 1. Solve the instance of the knapsack problem given above • 2. How can a solution be encoded as a (binary) chromosome?
Task • 1. Solve the instance of the knapsack problem given above... • 82 = 41+31+10 is one solution, there may be others • 2. How can a solution be encoded as a (binary) chromosome? • Each bit i in a chromosome encodes whether weight w[i] is present or not • Solution above: {5, 8, 10, 23, 27, 31, 37, 41} [0, 0, 1, 0, 0, 1, 0, 1]
The GA cycle chosen parents recombination children selection modification modified children parents evaluation population evaluated children deleted members discard
Mutation: local modification • Causes movement in the search space • Restores lost information to the population Before: (1 0 1 1 0 1 1 0) After: (1 0 1 0 0 1 1 0) Before: (1.38 -69.4 326.44 0.1) After: (1.38 -67.5 326.44 0.1)
Mutation • Given the representation for TSPs, how could we achieve mutation?
Mutation For TSPs, involves reordering of the list: ** Before: (5 8 7 2 1 6 3 4) After: (5 8 6 2 1 7 3 4)
Note • Both mutation and crossover are applied based on user-supplied probabilities • We usually use a fairly high crossover rate and fairly low mutation rate • Why do you think this is?
Evaluation of fitness • 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 modified children evaluation evaluated children
Fitness functions • Evaluate the ‘goodness’ of chromosomes • (How well they solve the problem) • Critical to the success of the GA • Often difficult to define well • Must be fairly fast, as every chromosome must be evaluated each generation (iteration)
Fitness functions • Fitness function for the TSP? • (3 5 7 2 1 6 4 8) • As we’re minimizing the distance travelled, the fitness is the total distance travelled in the journey defined by the chromosome
Fitness functions • The knapsack problem: fitness function? • given a set of weights W, and a target weight T, find a subset of W whose sum is as close to T as possible • Example: W = {5, 8, 10, 23, 27, 31, 37, 41} and T = 82
Deletion • Generational GA:entire populations replaced with each iteration • Steady-state GA:a few members replaced each generation population deleted members discard
The GA cycle chosen parents recombination children selection modification modified children parents evaluation population evaluated children deleted members discard
Stopping! • The GA cycle continues until • The system has ‘converged’; or • A specified number of iterations (‘generations’) has been performed
Good demo of the GA components • http://www.obitko.com/tutorials/genetic-algorithms/example-function-minimum.php
An abstract example Distribution of Individuals in Generation 0 Distribution of Individuals in Generation N
Examples • Eaters • http://math.hws.edu/xJava/GA/ • TSP • http://www.heatonresearch.com/articles/65/page1.html • http://www.ads.tuwien.ac.at/raidl/tspga/TSPGA.html • Good demo of the GA components • http://www.obitko.com/tutorials/genetic-algorithms/example-function-minimum.php
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 fitness function (often hardest part)
Benefits of GAs • Concept is easy to understand • Supports multi-objective optimization • Good for “noisy” environments • Always an answer; answer gets better with time • Inherently parallel; easily distributed