Estimation of Distribution Algorithms Framework (EDAF)

EDAF is a powerful and flexible Java framework for Estimation of Distribution Algorithms (EDAs) and other Evolutionary Algorithms (EAs). It provides a modular and extensible architecture that allows you to easily implement and experiment with different algorithms, genotypes, and operators.

This document provides a comprehensive overview of the framework, including its architecture, available components, and instructions on how to use it.

Karlo Knezevic 2013. Evolucijski algoritmi temeljeni na vjerojatnosnim razdiobama (Croatian language). Master thesis, Nr. 540, Faculty of Electrical Engineering and Computing, University of Zagreb. Google Scholar

Table of Contents

• Architecture
• Getting Started
• Configuration
• Algorithms
• Genotypes
• Operators
• Extending the Framework
• How to Build
• License

Architecture

The framework is designed with a modular and extensible architecture. The core components are decoupled, allowing you to easily swap them out or add new ones.

The following Mermaid diagram illustrates the high-level architecture of the framework:

graph TD
    A[Configuration] --> B{Component Factory};
    B --> C[Algorithm];
    B --> D[Problem];
    B --> E[Population];
    B --> F[Genotype];
    B --> G[Selection];
    B --> H[Statistics];
    B --> I[Termination Condition];

    C --> D;
    C --> E;
    C --> G;
    C --> H;
    C --> I;

    D --> F;
    E --> F;

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The ComponentFactory is responsible for creating all the necessary components based on the configuration file. The Problem component defines the optimization problem, including the fitness function and whether the goal is to MINIMIZE or MAXIMIZE the fitness. This allows you to easily configure your experiment without changing the source code.

Getting Started

To get started with EDAF, you need to provide a YAML configuration file that defines the experiment you want to run. You can use the generate-config command to generate a template configuration file:

java -jar examples.jar generate-config --algorithm gga > config.yaml

This will create a config.yaml file with a default configuration for the Generational Genetic Algorithm (gGA). You can then modify this file to suit your needs.

To run the experiment, use the run command:

java -jar examples.jar run --config config.yaml

Configuration

The configuration is done through a YAML file. The following sections describe the main configuration options.

Problem

The problem section defines the problem to be solved.

Parameter	Description
class	The fully qualified name of the problem class.
optimization	The optimization goal (MINIMIZE or MAXIMIZE). Defaults to MINIMIZE.
genotype	The genotype configuration.
parameters	A map of parameters for the problem.

Genotype

The genotype section defines the genotype representation.

Parameter	Description
type	The type of the genotype (binary, fp, integer, permutation, tree).
length	The length of the genotype.
lowerBound	The lower bound for floating-point genotypes.
upperBound	The upper bound for floating-point genotypes.
minBound	The minimum value for integer genotypes.
maxBound	The maximum value for integer genotypes.
maxDepth	The maximum depth for tree genotypes.
crossing	The crossover operator configuration.
mutation	The mutation operator configuration.

Algorithm

The algorithm section defines the algorithm to be used.

Parameter	Description
name	The name of the algorithm.
population	The population configuration.
selection	The selection operator configuration.
termination	The termination condition configuration.
elitism	The number of best individuals to carry over to the next generation.
parameters	A map of parameters for the algorithm.

Algorithms

The framework includes implementations of several popular evolutionary algorithms:

• gGA (Generational Genetic Algorithm): A traditional GA where the entire population is replaced in each generation.
• eGA (Eliminative Genetic Algorithm): A steady-state GA where one individual is replaced in each generation.
• cGA (Compact Genetic Algorithm): An EDA that simulates the behavior of a simple GA with a large population.
• UMDA (Univariate Marginal Distribution Algorithm): An EDA that assumes the variables are independent.
• PBIL (Population-Based Incremental Learning): An EDA that uses a probability vector to generate new individuals.
• MIMIC: An EDA that uses a chain-like probabilistic model.
• LTGA (Linkage Tree Genetic Algorithm): A GA that uses a linkage tree to guide crossover.
• BOA (Bayesian Optimization Algorithm): An EDA that uses a Bayesian network to model the distribution of promising solutions.
• BMDA (Bivariate Marginal Distribution Algorithm): An EDA that considers pairwise dependencies between variables.
• GP (Genetic Programming): An evolutionary algorithm that evolves computer programs.

Genotypes

The framework supports the following genotype representations:

• Binary: A genotype represented by a string of bits.
• Floating-point (fp): A genotype represented by a vector of real numbers.
• Integer: A genotype represented by a vector of integers.
• Permutation: A genotype represented by a permutation of integers.
• Tree: A genotype represented by a tree structure, used in Genetic Programming.

Operators

The framework provides a variety of operators for selection, crossover, and mutation.

Selection

• Tournament Selection: Selects individuals by running a tournament among a random subset of the population.
• Roulette Wheel Selection: Selects individuals with a probability proportional to their fitness.

Crossover

Genotype	Crossover Operators
Binary	onePoint, uniform
Integer	onePoint, twoPoint
Floating-point	sbx (Simulated Binary Crossover)
Permutation	pmx (Partially Mapped Crossover), ox (Order Crossover)
Tree	tree

Mutation

Genotype	Mutation Operators
Binary	simple
Integer	simple
Floating-point	polynomial
Permutation	swap, inversion
Tree	tree

Extending the Framework

The framework is designed to be easily extended. You can add new algorithms, genotypes, and operators by implementing the corresponding interfaces and registering them in the factories.

For more details, please refer to the Extending the Framework documentation.

How to Build

The project is built with Maven. To build the project, run the following command from the root directory:

mvn clean install

License

EDAF (Estimation of Distribution Algorithms Framework)
Copyright (C) 2025 Dr. Karlo Knežević

This project is licensed under the terms of the GNU General Public License v3.0.
See the LICENSE file for details.