Introduction to genetic algorithms with Java applets

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Main pageGENETICALGORITHMSThese pages introduce some fundamentals of genetics algorithms. Pages areintended to be used for learning about genetics algorithms without anyprevious knowledge from this area. Only some knowledge of computerprogramming is assumed. You can find here several interactive Java appletsdemonstrating work of genetic algorithms.As the area of genetics algorithms is very wide, it is not possible to covereverything in these pages. But you should get some idea, what the geneticalgorithms are and what they could be useful for. Do not expect anysophisticated mathematics theories here.Now please choose next to continue or you can choose any topic from themenu on the left side. If you do not want to read all the introducing chapters,you can skip directly to genetic algorithms and return later.You can also check recommendations for your browser.This site has also a Japanese translation.[This page without frames] [This page with frames](c) Marek Obitko, 1998http://cs.felk.cvut.cz/~xobitko/ga/main.html [7.5.2000 16:33:06]
Genetic algorithmIV. Genetic AlgorithmBasic DescriptionGenetic algorithms are inspired by Darwin's theory about evolution. Solution to a problem solved bygenetic algorithms is evolved.Algorithm is started with a set of solutions (represented by chromosomes) called population. Solutionsfrom one population are taken and used to form a new population. This is motivated by a hope, that thenew population will be better than the old one. Solutions which are selected to form new solutions(offspring) are selected according to their fitness - the more suitable they are the more chances they haveto reproduce.This is repeated until some condition (for example number of populations or improvement of the bestsolution) is satisfied.ExampleAs you already know from the chapter about search space, problem solving can be oftenexpressed as looking for extreme of a function. This is exactly what the problem shown hereis. Some function is given and GA tries to find minimum of the function.You can try to run genetic algorithm at the following applet by pressing button Start. Graphrepresents some search space and vertical lines represent solutions (points in search space).The red line is the best solution, green lines are the other ones.Button Start starts the algorithm, Step performs one step (i.e. forming one new generation),Stop stops the algorithm and Reset resets the population.Here is applet, but your browser does not support Java. If you want to see applets, please check browserrequirements.Outline of the Basic Genetic Algorithm1.2.3.[Start] Generate random population of n chromosomes (suitable solutions for the problem)[Fitness] Evaluate the fitness f(x) of each chromosome x in the population[New population] Create a new population by repeating following steps until the new populationis complete1. [Selection] Select two parent chromosomes from a population according to their fitness (thebetter fitness, the bigger chance to be selected)http://cs.felk.cvut.cz/~xobitko/ga/gaintro.html (1 of 2) [7.5.2000 16:33:06]
Page 2 and 3: About DNADNA (Deoxyribonucleic acid
Page 4 and 5: About DNA(c) Marek Obitko, 1998http
Page 6 and 7: IntroductionI. IntroductionFirst Wo
Page 8 and 9: Search Spaceresearchers (and maybe
Page 12 and 13: Genetic algorithm4.5.6.2. [Crossove
Page 14 and 15: Other resourcesAppendix: Other Reso
Page 16 and 17: RecommendationsApplications of GAGe
Page 18 and 19: Operators of GAThere are other ways
Page 20 and 21: Parameters of GAVII. Parameters of
Page 22 and 23: GA - 3D functionVIII. Extreme of Fu
Page 24 and 25: SelectionRank SelectionThe previous
Page 26 and 27: EncodingX. EncodingIntroductionEnco
Page 28 and 29: EncodingTree EncodingTree encoding
Page 30 and 31: Crossover and mutation11001011 + 11
Page 32 and 33: TSP ExampleXII. Travelling Salesman
Page 34 and 35: Frequently asked questionsFAQ - Fre
Page 36: Access statisticsAccess statistics

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