进化算法

blog
嗯，写个东西玩，这就是很像进化的进化算法.

进化算法是以达尔文的进化论思想为基础，通过模拟生物进化过程与机制的求解问题的自组织、自适应的人工智能技术。生物进化是通过繁殖、变异、竞争和选择实现的；而进化算法则主要通过选择、重组和变异这三种操作实现优化问题的求解。

首先生成一个随机位置的＂太阳＂，然后生成一堆位置随机会移动会光合作用，会呼吸作用的生命．靠近＂太阳＂将可以进行＂光合作用＂，否则就只能进行＂呼吸作用＂消耗生命，当生命值消耗完就die了，当生命值很健康时有一定几率产生后代.

import random
import math
import pylab
import matplotlib.animation as animation
#一个太阳
sunCoordX = random.randint(1,99)
sunCoordY = random.randint(1,99)
#一堆生命
lifes = []

class Life(object):
 """一个会移动会光合会呼吸的生命"""
 def __init__(self, name):
  #super(Life, self).__init__()
  self.name = name
  self.x = random.randint(1,99)
  self.y = random.randint(1,99)
  self.hp = random.randint(0,99)
  self.agility = random.randint(1,5)
  self.cos = self.cosWithSun()
  #一系列生化反应
 def live(self):
  self.move()
  self.costHp()
  self.photosynthesis()
  self.birth()
  #运动
 def move(self):
  self.x += round(random.randint(-self.agility,self.agility))
  self.y += round(random.randint(-self.agility,self.agility))
  if(self.x<=0):
   self.x = 1
  if(self.y<=0):
   self.y = 1
  self.cos = self.cosWithSun()
  if(self.cos<5 and self.agility<10):
   self.agility +=1
   

 def printMe(self):
  if(self.hp>0):
   print "%s" % (self.name)
   print "[cos:%f,x:%d,y:%d,hp:%d,agility:%d]" % (self.cos,self.x,self.y,self.hp,self.agility)
 #靠近太阳　光合作用
 def photosynthesis(self):
  self.cos = self.cosWithSun()
  if(self.cos<15):
   #print "photosynthesis"
   self.hp +=11
if(self.agility>1):
    self.agility -=1
 #呼吸　消耗生命值
 def costHp(self):
  self.hp -=10
  if(self.hp<=0):
   self.die()
 def die(self):
  print "%s die"%(self.name)
  lifes.remove(self)
 def cosWithSun(self):
  cos = math.sqrt((sunCoordX-self.x)**2+(sunCoordY-self.y)**2)
  #print "%d %d %d %d %d" % (sunCoordX,sunCoordY,self.x,self.y,cos)
  return cos
 #生小的
 def birth(self):
  if(random.randint(0,10)<2 and self.hp>70 and len(lifes)<2000):
   child = Life(self.name+"_child")
   child.x=self.x
   child.y=self.y
   child.hp=self.hp+1
   child.agility=self.agility
   lifes.append(child)

j=0
while(j<50):
 life = Life("life%d"%(j))
 lifes.append(life)
 j+=1

fig = pylab.figure()  
scat1 = pylab.scatter(1, 1, s=10,color='blue')  
scat2 = pylab.scatter(1, 1, s=50,color='red')  
pylab.xticks([0, 25, 50, 75, 100])
pylab.yticks([0, 25, 50, 75, 100])
def setup_plot():  
 return scat1,scat2

def update_plot(i):
 print "lives num %d"%len(lifes)
 data = []
 for l in lifes:
  #l.printMe()
  data.append(l.x)
  data.append(l.y)
  l.live()
 scat1.set_offsets([data])
 scat2.set_offsets([sunCoordX,sunCoordY])
 return scat1, scat2

ani = animation.FuncAnimation(fig, update_plot, interval=50, init_func=setup_plot, blit=True)
pylab.show()