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import numpy as np
import random
from deap import base, creator, tools, algorithms
import plotly.graph_objs as go
import streamlit as st
# Define the objective function
class Objective:
def Evaluate(self, p):
return -25.7743 * np.exp(-0.5 * ((p[0] + 25.7743)**2 / 0.1 + (p[1] - 80.1937)**2 / 0.1)) + \
80.1937 * np.exp(-0.5 * ((p[0] - 25.7743)**2 / 0.2 + (p[1] + 80.1937)**2 / 0.2)),
# Define the bounds
BOUND_LOW, BOUND_UP = -100.0, 100.0
NDIM = 2
# Create the fitness and individual classes
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin)
# Initialize the toolbox
toolbox = base.Toolbox()
# Attribute generator
toolbox.register("attr_float", random.uniform, BOUND_LOW, BOUND_UP)
# Structure initialization
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, NDIM)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
# Define the evaluation function
def evaluate(individual):
return Objective().Evaluate(individual)
toolbox.register("evaluate", evaluate)
# Define the genetic operators
toolbox.register("mate", tools.cxBlend, alpha=0.5)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)
toolbox.register("select", tools.selTournament, tournsize=3)
# Define the main function to run the genetic algorithm
def main():
random.seed(42)
# Create an initial population of 300 individuals
pop = toolbox.population(n=300)
# Define the hall of fame to keep track of the best individuals
hof = tools.HallOfFame(1)
# Define the statistics to collect
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean)
stats.register("std", np.std)
stats.register("min", np.min)
stats.register("max", np.max)
# Define the parameters for the genetic algorithm
ngen = 500
cxpb = 0.7
mutpb = 0.2
# Run the genetic algorithm
pop, log = algorithms.eaSimple(pop, toolbox, cxpb, mutpb=mutpb, ngen=ngen, stats=stats, halloffame=hof, verbose=True)
# Print the best individual
st.write("Best individual is: %s, %s" % (hof[0], hof[0].fitness.values))
st.image('Objective Function 3.jpeg', caption='Objective Function 2 Target Pattern 2')
# Visualization
x = np.linspace(-100, 100, 400)
y = np.linspace(-100, 100, 400)
X, Y = np.meshgrid(x, y)
Z = np.zeros_like(X)
for i in range(X.shape[0]):
for j in range(X.shape[1]):
Z[i, j] = Objective().Evaluate([X[i, j], Y[i, j]])[0]
# Custom colorscale with specified colors
custom_colorscale = [
[0, '#ffffff'], # White
[1, '#0000ff'] # Blue
]
trace = go.Surface(x=X, y=Y, z=Z, colorscale=custom_colorscale)
data = [trace]
# Add convergence path
convergence_path = np.array([ind for ind in hof])
trace_convergence = go.Scatter3d(
x=convergence_path[:, 0],
y=convergence_path[:, 1],
z=[Objective().Evaluate(ind)[0] for ind in convergence_path],
mode='lines+markers',
marker=dict(size=4, color='#0000ff', showscale=True), # Blue markers
line=dict(color='#ffffff', width=2) # White line
)
data.append(trace_convergence)
# Add target pattern coordinates
target_pattern = np.array([
[-25.7743, 80.1937]
])
trace_target = go.Scatter3d(
x=target_pattern[:, 0],
y=target_pattern[:, 1],
z=[Objective().Evaluate(ind)[0] for ind in target_pattern],
mode='markers',
marker=dict(size=10, color='#0000ff', symbol='x'), # Blue markers
name='Target Pattern'
)
data.append(trace_target)
# Add best individual
trace_best = go.Scatter3d(
x=[hof[0][0]],
y=[hof[0][1]],
z=[hof[0].fitness.values[0]],
mode='markers',
marker=dict(size=10, color='#ff0000', symbol='circle'), # Red marker
name='Best Individual'
)
data.append(trace_best)
layout = go.Layout(
title='Objective Function Visualization with Convergence Path',
scene=dict(
xaxis=dict(title='X'),
yaxis=dict(title='Y'),
zaxis=dict(title='Z')
)
)
fig = go.Figure(data=data, layout=layout)
st.plotly_chart(fig)
# Convergence Plot
min_fitness_values = log.select("min")
avg_fitness_values = log.select("avg")
convergence_trace = go.Scatter(
x=list(range(len(min_fitness_values))),
y=min_fitness_values,
mode='lines+markers',
name='Min Fitness'
)
avg_trace = go.Scatter(
x=list(range(len(avg_fitness_values))),
y=avg_fitness_values,
mode='lines+markers',
name='Avg Fitness'
)
convergence_layout = go.Layout(
title='Convergence Plot',
xaxis=dict(title='Generation'),
yaxis=dict(title='Fitness')
)
convergence_fig = go.Figure(data=[convergence_trace, avg_trace], layout=convergence_layout)
st.plotly_chart(convergence_fig)
# Population Distribution Over Generations
def plot_population_distribution(pop, generation):
pop_trace = go.Scatter3d(
x=[ind[0] for ind in pop],
y=[ind[1] for ind in pop],
z=[ind.fitness.values[0] for ind in pop],
mode='markers',
marker=dict(size=4, color='#ff0000', showscale=True), # Red markers
name=f'Generation {generation}'
)
return pop_trace
# Interactive Controls for Population Distribution
generation = st.slider("Select Generation", min_value=0, max_value=ngen, value=150)
pop_trace = plot_population_distribution(pop, generation)
pop_fig = go.Figure(data=[trace, pop_trace], layout=layout)
st.plotly_chart(pop_fig)
if __name__ == "__main__":
st.title("Genetic Algorithm Objective Function 2 Pair of Gaussians Visualization -1*[-25.7743, 80.1937]")
st.image('Objective Function 2.jpeg', caption='UAV Objective Function 2 Target Pattern')
st.image('Objective Function 2.png', caption='UAV Objective Function 2')
st.write('一。NICO MAULEN | Techengue Vol 5 - Tech & Latin House (Live Set)')
main() |