Optimization Algorithms: Genetic Algorithms vs. Gradient Descent

Optimization is at the heart of machine learning and many scientific computing applications. This study compares two fundamentally different approaches: gradient-based methods and evolutionary algorithms.

Gradient Descent

Gradient descent is the workhorse of modern machine learning, particularly in training neural networks.

How it works

1. Compute the gradient of the loss function
2. Update parameters in the direction of steepest descent
3. Repeat until convergence

Variants

• Stochastic Gradient Descent (SGD): Uses random samples for faster updates
• Adam: Adaptive learning rates with momentum
• RMSprop: Root mean square propagation

Limitations

• Can get stuck in local minima
• Requires differentiable objective functions
• Sensitive to learning rate selection

Genetic Algorithms

Genetic algorithms are inspired by natural evolution and can handle non-differentiable, non-convex problems.

Components

1. Population: Set of candidate solutions
2. Selection: Choose fittest individuals for reproduction
3. Crossover: Combine parents to create offspring
4. Mutation: Random changes for diversity

Advantages

• No gradient computation required
• Can escape local optima
• Naturally parallelizable
• Works with discrete and continuous variables

Comparative Analysis

My Research Application

In my work on cellular spheroid localization, I combine both approaches:

1. Use genetic algorithms for neural network architecture search
2. Apply gradient descent for weight optimization
3. This hybrid approach yields better results than either method alone

Conclusion

The best optimization strategy depends on the problem characteristics. For complex, non-convex landscapes common in real-world applications, hybrid approaches often provide the best balance of exploration and exploitation.

Research conducted at Mohammed V University of Rabat, Faculty of Sciences.