Go语言如何实现遗传算法

if settings.KeepBestAcrossPopulation{

newPopIndex = 1

}

for ; newPopIndex < settings.PopulationSize; newPopIndex++ {

indexSelection1 := rand.Int() % len(probabilisticListOfPerformers)

indexSelection2 := rand.Int() % len(probabilisticListOfPerformers)

// crossover

newIndividual := geneticAlgoRunner.PerformCrossover(

probabilisticListOfPerformers[indexSelection1],

probabilisticListOfPerformers[indexSelection2], settings.CrossoverRate)

// mutate

if rand.Intn(101) < settings.MutationRate {

newIndividual = geneticAlgoRunner.PerformMutation(newIndividual)

}

newPopulation = append(newPopulation, newIndividual)

}

population = newPopulation

// sort by performance

geneticAlgoRunner.Sort(population)

// keep the best so far

bestSoFar = population[len(population) - 1]

}

return bestSoFar, nil

}

func createStochasticProbableListOfIndividuals(population []interface{}) []interface{} {

totalCount, populationLength:= 0, len(population)

for j:= 0; j < populationLength; j++ {

totalCount += j

}

probableIndividuals := make([]interface{}, 0, totalCount)

for index, individual := range population {

for i:= 0; i < index; i++{

probableIndividuals = append(probableIndividuals, individual)

}

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