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Exercise 1 Francesco Abate Niccolo` Battezzati Miguel Kaouk Apprendimento mimetico

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EP – Program Flow Generate first population Generate new population by mutation Selection by tournament Goal? Max iterations? END μ μ q q σ, c.MAX_ITER μ + μ YES NO Fitness evaluation

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EP – Program Architecture ep.conf EvoConfigParser EvoConfigurator main EvoAgent ( float x[D] ) float evaluate_fitness(float (*fitnessFnc)(EvoAgent *)) bool termination(bool (*terminationFnc)(EvoAgent *))

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Experimental results μ q mean # of fitness evaluations σ = 0.8σ = 1.0σ = 2.5 10 n.s.237946379698 100 10n.s.227399560768 50n.s.2088101712028 D = 2, static σ

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Experimental results μ mean # of fitness evaluations q = 10q = 50 103050/ 1001863330033 10006166656000 D = 2, dynamic σ

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Experimental results μ mean # of fitness evaluations mean # of generations q = 10q = 50 10508475084// 10026431026423109203109 10002008700200816612001661 D = 5, dynamic σ

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Experimental results μ mean # of fitness evaluations q = 10q = 50 10140058/ 100851783823050 100055675006165000 D = 10, dynamic σ

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Section 7.2. Mean of a probability distribution is the long- run average outcome, µ, or µ x. Also called the expected value of x, or E(X). µ x = x i P.

Section 7.2. Mean of a probability distribution is the long- run average outcome, µ, or µ x. Also called the expected value of x, or E(X). µ x = x i P.

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