What is an estimation of distribution algorithm in C and how is it implemented?

Table of Contents

• Introduction
• Key Concepts in Estimation of Distribution Algorithms
  • Probabilistic Modeling
  • Algorithm Workflow
• Implementing an EDA in C
  • Example Implementation
  • Explanation
• Conclusion

Introduction

Estimation of Distribution Algorithms (EDAs) are optimization algorithms that use probabilistic models to guide the search for optimal solutions. Instead of relying on traditional genetic operators like crossover and mutation, EDAs build and update probabilistic models of promising solutions. These models are then used to generate new candidate solutions. This guide covers the fundamental concepts of EDAs and provides a practical example of implementing an EDA in C.

Key Concepts in Estimation of Distribution Algorithms

Probabilistic Modeling

In EDAs, probabilistic models represent the distribution of high-quality solutions. These models are updated based on the best solutions found in previous iterations. The goal is to capture the underlying distribution of successful solutions and use this information to generate new candidates that are more likely to be optimal.

Algorithm Workflow

1. Initialization: Generate an initial population of candidate solutions.
2. Evaluation: Assess the fitness of each solution.
3. Model Building: Construct a probabilistic model based on the high-quality solutions.
4. Sampling: Generate new candidate solutions using the probabilistic model.
5. Selection: Choose the best solutions to update the model.
6. Iteration: Repeat the process until convergence or a stopping criterion is reached.

Implementing an EDA in C

Example Implementation

Below is a basic implementation of an Estimation of Distribution Algorithm (EDA) in C:

Explanation

1. Objective Function: Defines the function to be minimized (e.g., the sphere function).
2. Initialization: Generates an initial population of random solutions.
3. Model Building: Calculates the mean of selected high-quality solutions to create a probabilistic model.
4. Sampling: Generates new solutions based on the probabilistic model.
5. Selection: Chooses the best solutions to update the model.
6. Iteration: Repeats the process to optimize the objective function.

Conclusion

Estimation of Distribution Algorithms (EDAs) use probabilistic models to guide the search for optimal solutions by learning from promising candidate solutions. Implementing an EDA in C involves defining the objective function, initializing the population, building a probabilistic model, and sampling new solutions based on that model. This approach can efficiently solve complex optimization problems by leveraging probabilistic modeling to explore the solution space effectively.