编写一段求解函数最大值的遗传算法的python代码

以下是一段简单的遗传算法求解函数最大值的Python代码：

import random

# 定义函数
def f(x):
    return x * x + 3 * x - 4

# 定义参数
POPULATION_SIZE = 50
NUM_GENERATIONS = 1000
TOURNAMENT_SIZE = 3
MUTATION_RATE = 0.01

# 定义个体类
class Individual:
    def __init__(self, x):
        self.x = x
        self.fitness = f(x)
        
    def mutate(self):
        if random.random() < MUTATION_RATE:
            self.x += random.uniform(-1, 1)
            self.fitness = f(self.x)
        
    def __lt__(self, other):
        return self.fitness > other.fitness

# 定义遗传算法类
class GeneticAlgorithm:
    def __init__(self):
        self.population = [Individual(random.uniform(-10, 10)) for _ in range(POPULATION_SIZE)]
        
    def evolve(self):
        for i in range(NUM_GENERATIONS):
            self.selection()
            self.crossover()
            for individual in self.population:
                individual.mutate()
            
        return max(self.population)
    
    def selection(self):
        for i in range(POPULATION_SIZE):
            tournament = random.sample(self.population, TOURNAMENT_SIZE)
            winner = max(tournament)
            self.population[i] = winner
            
    def crossover(self):
        for i in range(0, POPULATION_SIZE, 2):
            parent1 = self.population[i]
            parent2 = self.population[i+1]
            child1_x = (parent1.x + parent2.x) / 2
            child2_x = (parent1.x + parent2.x) / 2
            child1 = Individual(child1_x)
            child2 = Individual(child2_x)
            self.population[i] = child1
            self.population[i+1] = child2

# 运行遗传算法
ga = GeneticAlgorithm()
print(ga.evolve().x)

这段代码使用了遗传算法来求解函数 f(x) = x^2 + 3x - 4 的最大值。通过定义个体类和遗传算法类，我们可以轻松地实现遗传算法的各个过程，包括选择、交叉和变异。在主函数中，我们创建了一个遗传算法对象 ga，并调用它的 evolve 方法来运行遗传算法。最终，我们输出得到的最大值的 x 坐标。