Genetic Algorithms Czech Technical University in Prague, Faculty of Electrical Engineering Ondřej Vaněk, Agent Technology Center ZUI 2011.

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Presentation transcript:

Genetic Algorithms Czech Technical University in Prague, Faculty of Electrical Engineering Ondřej Vaněk, Agent Technology Center ZUI 2011

Evolution in nature

Population of individuals Each individual has a fitness (how good he performs in nature) The individuals are selected based on the fitness They breed by combining genetic information New population with a few mutated individuals Old population is replaced by the new one. Evolution in nature

Population of individuals Each individual has a fitness (how good he performs in nature) The individuals are selected based on the fitness They breed by combining genetic information New population with a few mutated individuals Old population is replaced by the new one. Genetic algorithms Candidate Solutions Candidate Problem Solution representation

Candidate Solution – member of a set of possible solutions to a given problem (does not have to be reasonable, it just satisfies the constraints). Population – a set of candidate solutions. Fitness - a measure of performance of a solution. – Function – Algorithm – Black Box Explanation of Terms

1.Initialization 2.Selection 3.Reproduction a.Crossover b.Mutation 4.Replacement 5.Termination Algorithm Template

1.Initialization 2.Selection 3.Reproduction a.Crossover b.Mutation 4.Replacement 5.Termination Algorithm Template - Initialization Create the initial population of candidate solutions How large should the population be? Generate Randomly? How? Incorporate domain knowledge? Cover range of solutions, problem dependent Uniform distribution Seed in promising areas Uniform distribution Seed in promising areas YES!

1.Initialization 2.Selection 3.Reproduction a.Crossover b.Mutation 4.Replacement 5.Termination Algorithm Template - Initialization Select the promising candidates for reproduction or survival How many should I choose? Choose Randomly? How? Size of the population should remain constant Roulette wheel selection, sampling,…

1.Initialization 2.Selection 3.Reproduction a.Crossover b.Mutation 4.Replacement 5.Termination Algorithm Template - Initialization Combine the selected candidates to produce an offspring I am afraid it will break some good candidates How to reproduce? What are the parallels with the nature? Don’t be Schemes are here to save you… Don’t be Schemes are here to save you… 1-,2-point crossover, uniform, arithmetic, … Meiosis, genetic recombination

1.Initialization 2.Selection 3.Reproduction a.Crossover b.Mutation 4.Replacement 5.Termination Algorithm Template - Initialization Which one is better? Problem dependent

1.Initialization 2.Selection 3.Reproduction a.Crossover b.Mutation 4.Replacement 5.Termination Algorithm Template - Initialization How much to mutate? High mutation rate = random search zero mutation rate = can stuck in local minima High mutation rate = random search zero mutation rate = can stuck in local minima

1.Initialization 2.Selection 3.Reproduction a.Crossover b.Mutation 4.Replacement 5.Termination Algorithm Template - Initialization Place new offspring into the current population Generate completely new ones? Replace all? You can (initialization phase)… Elitism, generations, keep 20%,…

1.Initialization 2.Selection 3.Reproduction a.Crossover b.Mutation 4.Replacement 5.Termination Algorithm Template - Initialization When the optimum is reached, terminate the algorithm What to do next? How will I find out? I am done, restart algorithm, … Eps-optimum, value of the best candidate, …

Relation to other techniques - QUIZ 1.GA is (???)

Relation to other techniques - QUIZ 1.GA is SEARCH algorithm 2.What kind of search?

Relation to other techniques - QUIZ 1.GA is SEARCH algorithm 2.GA is Heuristic (Informed) search algorithm 3.What is GA looking for?

Relation to other techniques - QUIZ 1.GA is SEARCH algorithm 2.GA is Heuristic (Informed) search algorithm 3.GA is looking for a global optimum