Exercise 1

Francesco AbateNiccolo` BattezzatiMiguel Kaouk

Apprendimento mimetico

EP – Program Flow

Generate first population

Generate new population by

mutation

Selection by tournament

Goal?

Max iterations?

END

μμ

qq

σ, cσ, c

.MAX_ITER.MAX_ITER

μ + μμ + μ

YES

YES

NO

NO

Fitness evaluation

EP – Program Architecture

ep.confEvoConfigParser

EvoConfigurator

main

EvoAgent

( float x[D] )

float evaluate_fitness(float (*fitnessFnc)(EvoAgent *))float evaluate_fitness(float (*fitnessFnc)(EvoAgent *))bool termination(bool (*terminationFnc)(EvoAgent *))bool termination(bool (*terminationFnc)(EvoAgent *))

Experimental results

μ qmean # of fitness evaluations

σ = 0.8 σ = 1.0 σ = 2.5

10 10 n.s. 237946 379698

10010 n.s. 227399 560768

50 n.s. 208810 1712028

D = 2, static σ

Experimental results

μmean # of fitness evaluations

q = 10 q = 50

10 3050 /

100 18633 30033

1000 61666 56000

D = 2, dynamic σ

Experimental results

μ

mean # of fitness evaluations

mean # of generations

q = 10 q = 50

10 50847 5084 / /

100 264310 2642 310920 3109

1000 2008700 2008 1661200 1661

D = 5, dynamic σ

Experimental results

μmean # of fitness evaluations

q = 10 q = 50

10 140058 /

100 851783 823050

1000 5567500 6165000

D = 10, dynamic σ