Please write a genetic algorithm in Python to calculate the maximum value of function f (x)=0.4+sinc (4x)+1.1sinc (4x+2)+0.8sinc (X-2)+0.7sinc (6x-4) between -2 and 2. The function solution is represented by a 16 bit binary number. The population size is 30, the crossover probability is 0.3, the mutation probability is 0.01, and the number of iterations is 400

Sure, here's an implementation of the genetic algorithm you described:

import random
import math

def fitness_function(x):
    return 0.4 + math.sin(4 * x) / (4 * x) + 1.1 * math.sin(4 * x + 2) / (4 * x + 2) + \
           0.8 * math.sin(x - 2) / (x - 2) + 0.7 * math.sin(6 * x - 4) / (6 * x - 4)

def binary_to_float(binary_num):
    return -2 + (4 / (2 ** 16 - 1)) * int(binary_num, 2)

def generate_population(population_size):
    population = []
    for i in range(population_size):
        individual = ''.join(random.choice(['0', '1']) for _ in range(16))
        population.append(individual)
    return population

def selection(population):
    fitness_scores = [fitness_function(binary_to_float(individual)) for individual in population]
    total_fitness = sum(fitness_scores)
    probabilities = [fitness / total_fitness for fitness in fitness_scores]
    selected = random.choices(population, probabilities, k=2)
    return selected[0], selected[1]

def crossover(parent1, parent2, crossover_prob):
    if random.random() < crossover_prob:
        crossover_point = random.randint(1, 14)
        child1 = parent1[:crossover_point] + parent2[crossover_point:]
        child2 = parent2[:crossover_point] + parent1[crossover_point:]
        return child1, child2
    else:
        return parent1, parent2

def mutation(individual, mutation_prob):
    mutated = ''
    for bit in individual:
        if random.random() < mutation_prob:
            mutated += '0' if bit == '1' else '1'
        else:
            mutated += bit
    return mutated

def genetic_algorithm(population_size, crossover_prob, mutation_prob, num_iterations):
    population = generate_population(population_size)
    for i in range(num_iterations):
        new_population = []
        for j in range(population_size // 2):
            parent1, parent2 = selection(population)
            child1, child2 = crossover(parent1, parent2, crossover_prob)
            child1 = mutation(child1, mutation_prob)
            child2 = mutation(child2, mutation_prob)
            new_population.extend([child1, child2])
        population = new_population
    best_individual = max(population, key=lambda x: fitness_function(binary_to_float(x)))
    return binary_to_float(best_individual)

You can call this function with the parameters you specified to get the maximum value of the given function:

result = genetic_algorithm(population_size=30, crossover_prob=0.3, mutation_prob=0.01, num_iterations=400)
print(result)

Note that the solution may not always be the same due to the random nature of the genetic algorithm.