sia's Introduction
Los parámetros recibidos por la función "genetic" son: % % S = puntos de la funcion a aprender % layers = cantidad de neuronas x capa en forma de array % replacement = metodo de reemplazo (1 2 o 3) % N = cantidad de populantes % K = cantidad de poblacion que pasa a la primera etapa en los metodos 2 y 3 % maxGen = cantidad de generaciones % mut = parametros de mutacion [mutP c genP] % mutP = probabilidad de mutar % c = valor por el que se mutiplica mutP % genP = cada cuantas generaciones % back = parametros de backpropagation[backP times] % backP = probabilidad de hacer backpropagation % times = cantidad de iteraciones % crossP = probabilidad de cruce % selectionCrits = criterios de seleccion, en forma de array donde cada numero representa: % 1 = elitismo % 2 = ruleta % 3 = torneo % 4 = boltzman % 5 = elite-ruleta % 6 = elite-boltzman % cross = funcion de cruzamiento % f = fitness pretendido: corta si se alcanza % fitestCond = condicion de corte de contenido: [fitChange, fitestGens] % fitChange = cambio que debe haber para que corte % fitestGens = cantidad de generaciones sin cambios % meanCond = condiciond e corte por estructura: [meanChange, meanGens] % meanChange = cambio que debe haber para que no corte % meanGens = cantidad de generaciones sin cambios % fitnessOpt = modo de fitness : 1 para 1/ecm, 2 para -ecm ejemplo de ejecución: genetic(S(1:500), 3, 50, 40, 50, [0.1 0 0], [0.05 1000], 0.9, [3 3], @piolaCross, 20000000, [0.1 0], [0.1 0], 1)
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