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The article discusses the historical context of AI and genetic algorithms, highlighting a Fortran program written in 2001 for simulating a two-link pendulum using genetic algorithms. The research paper and source code are available on GitHub. The author plans to modernize the code by rewriting it in Python.
You Should Know:
Genetic algorithms (GAs) are optimization techniques inspired by natural selection. Below are practical implementations and commands to experiment with genetic algorithms in Python and Linux environments.
Python Implementation of a Basic Genetic Algorithm
import random
def generate_individual(length):
return [random.randint(0, 1) for _ in range(length)]
def fitness(individual):
return sum(individual)
def crossover(parent1, parent2):
split_point = random.randint(1, len(parent1)-1)
child1 = parent1[:split_point] + parent2[split_point:]
child2 = parent2[:split_point] + parent1[split_point:]
return child1, child2
def mutate(individual, mutation_rate=0.01):
for i in range(len(individual)):
if random.random() < mutation_rate:
individual[bash] ^= 1
return individual
population = [generate_individual(10) for _ in range(100)]
for generation in range(100):
population = sorted(population, key=lambda x: -fitness(x))
next_gen = population[:20] Elite selection
while len(next_gen) < 100:
parent1, parent2 = random.choices(population[:50], k=2)
child1, child2 = crossover(parent1, parent2)
next_gen.extend([mutate(child1), mutate(child2)])
population = next_gen
print("Best individual:", max(population, key=fitness))
Linux Commands for AI & Genetic Algorithm Development
1. Install Python and Required Libraries
sudo apt update sudo apt install python3 python3-pip pip3 install numpy matplotlib
2. Run a Genetic Algorithm Script
python3 genetic_algorithm.py
3. Monitor System Performance During Execution
top -d 1
4. Parallel Execution with GNU Parallel
seq 10 | parallel -j4 python3 genetic_algorithm.py
5. Analyze Memory Usage
free -h
Windows Commands for AI Development
1. Install Python on Windows
winget install Python.Python.3.10
2. Run a Genetic Algorithm in PowerShell
py genetic_algorithm.py
3. Check CPU Usage
Get-WmiObject Win32_Processor | Select LoadPercentage
What Undercode Say
Genetic algorithms remain a powerful tool in optimization problems, from cybersecurity (e.g., password cracking) to AI-driven simulations. Modernizing legacy Fortran code to Python enhances accessibility. Key takeaways:
– Use `numpy` for efficient numerical operations.
– Leverage Linux commands (top, free, parallel) for performance monitoring.
– Experiment with mutation rates and selection strategies for better convergence.
Expected Output:
Best individual: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
For the full Fortran-to-Python conversion, visit the GitHub repository.
References:
Reported By: Matei Anthony – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
