遗传算法中使用Golang实现的轮盘赌选择

huangapple
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英文:

Roulette Wheel Selection in Genetic Algorithm using Golang

问题

我正在为遗传算法构建一个模拟轮盘赌选择函数。首先，我想在主函数中计算fitnessScore的总和sum。在计算完fitnessScore的总和后，我想使用Go语言中的math/rand包从该总和中随机选择一个值。在这种情况下，我应该如何使用rand包？如何修复spin_wheel := rand.sum以随机选择一个值？

  1. package main
  3. import (
  4. 	"fmt"
  5. 	"time"
  6. 	"math/rand"
  7. )
  9. func rouletteWheel(fitnessScore []float64) []float64 {
  10. 	sum := 0.0
  11. 	for i := 0; i < len(fitnessScore); i++ {
  12. 		sum += fitnessScore[i]
  13. 	}
  15. 	rand.Seed(time.Now().UnixNano())
  16. 	spin_wheel := rand.Float64() * sum
  17. 	partial_sum := 0.0
  18. 	for i := 0; i < len(fitnessScore); i++ {
  19. 		partial_sum += fitnessScore[i]
  20. 		if partial_sum >= spin_wheel {
  21. 			return fitnessScore
  22. 		}
  23. 	}
  24. 	return fitnessScore
  25. }
  27. func main() {
  28. 	fitnessScore := []float64{0.1, 0.2, 0.3, 0.4}
  29. 	fmt.Println(rouletteWheel(fitnessScore))
  30. }

在这个代码中，你可以使用rand.Float64()函数生成一个0到1之间的随机浮点数，并将其乘以sum来获取一个在0到sum之间的随机值。这样，你就可以使用spin_wheel来进行随机选择了。

英文:

I'm building a mock roulette wheel selection function for genetic algorithm. First of, I would want to add up the sum of the fitnessScore in the main function. After adding up the fitnessScore I wanted to randomize a value out of that sum using the math/rand package in Go. How should I use the rand package in this scenario how do I fix spin_wheel := rand.sum in order to random a value?

  1. package main
  3. import(
  4. 	&quot;fmt&quot;
  5. 	&quot;time&quot;
  6. 	&quot;math/rand&quot;
  7. )
  9. func rouletteWheel(fitnessScore []float64) []float64{
  10. 	sum := 0.0
  11. 	for i := 0; i &lt; len(fitnessScore); i++ {
  12. 		sum += fitnessScore[i]
  13. 	}
  14. 	
  15. 	rand.Seed(time.Now().UnixNano())
  16. 	spin_wheel := rand.sum
  17. 	partial_sum := 0.0
  18. 	for i := 0; i &lt; len(fitnessScore); i++{
  19. 		partial_sum += fitnessScore[i]
  20. 		if(partial_sum &gt;= spin_wheel){
  21. 			return fitnessScore
  22. 		}
  23. 	}
  24. 	return fitnessScore
  25. }
  27. func main(){
  28. 	fitnessScore := []float64{0.1, 0.2, 0.3, 0.4}
  29. 	fmt.Println(rouletteWheel(fitnessScore))
  30. }

答案1

得分: 1

例如，

  1. package main
  3. import (
  4.     "fmt"
  5.     "math/rand"
  6.     "time"
  7. )
  9. // 根据权重（概率）返回所选的权重
  10. // 适应度比例选择：
  11. // https://en.wikipedia.org/wiki/Fitness_proportionate_selection
  12. func rouletteSelect(weights []float64) float64 {
  13.     // 计算总权重
  14.     sum := 0.0
  15.     for _, weight := range weights {
  16.         sum += weight
  17.     }
  18.     // 获取一个随机值
  19.     value := rand.Float64() * sum
  20.     // 根据权重定位随机值
  21.     for _, weight := range weights {
  22.         value -= weight
  23.         if value <= 0 {
  24.             return weight
  25.         }
  26.     }
  27.     // 仅在出现舍入误差时
  28.     return weights[len(weights)-1]
  29. }
  31. func main() {
  32.     rand.Seed(time.Now().UnixNano())
  33.     weights := []float64{0.1, 0.2, 0.3, 0.4}
  34.     fmt.Println(rouletteSelect(weights))
  35. }
英文:

For example,

  1. package main
  3. import (
  4. 	&quot;fmt&quot;
  5. 	&quot;math/rand&quot;
  6. 	&quot;time&quot;
  7. )
  9. // Returns the selected weight based on the weights(probabilities)
  10. // Fitness proportionate selection:
  11. // https://en.wikipedia.org/wiki/Fitness_proportionate_selection
  12. func rouletteSelect(weights []float64) float64 {
  13. 	// calculate the total weights
  14. 	sum := 0.0
  15. 	for _, weight := range weights {
  16. 		sum += weight
  17. 	}
  18. 	// get a random value
  19. 	value := rand.Float64() * sum
  20. 	// locate the random value based on the weights
  21. 	for _, weight := range weights {
  22. 		value -= weight
  23. 		if value &lt;= 0 {
  24. 			return weight
  25. 		}
  26. 	}
  27. 	// only when rounding errors occur
  28. 	return weights[len(weights)-1]
  29. }
  31. func main() {
  32. 	rand.Seed(time.Now().UnixNano())
  33. 	weights := []float64{0.1, 0.2, 0.3, 0.4}
  34. 	fmt.Println(rouletteSelect(weights))
  35. }

huangapple
  • 本文由 发表于 2015年10月8日 07:11:58
  • 转载请务必保留本文链接：https://go.coder-hub.com/33003974.html
  • algorithm
  • artificial-intelligence
  • go
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