Evolutionary Algorithms GLEAM and HyGLEAM

GLEAM is an Evolutionary Algorithm (EA) of its own and HyGLEAM is its memetic extension.

GLEAM at a Glance

GLEAM uses chromosomes whose genes can be easily configured in an application-specific way, so that a gene can determine all (relevant) properties of a given phenotypic element. For example, in a scheduling problem, this could be the resources to be used in a scheduling operation, e.g. a workstation or a classroom and an appropriate tutor. GLEAM works with three types of chromosomes: Fixed-length chromosomes with or without meaning-bearing order, and dynamic-length chromosomes with meaning-bearing order. In GLEAM, fitness calculation is based on the weighted sum. To control the selective pressure, partner selection is performed with linear ranking. Furthermore, instead of the otherwise common panmictic population, a structured one based on the neighborhood model is used. The self-adaptive balance between breadth-first and depth-first search thus achieved efficiently counteracts premature convergence. The neighborhood model can be viewed as a linear variant of cellular EAs. The memetic extension of GLEAM called HyGLEAM integrates local search procedures or heuristics to speed up evolutionary search.

Further details and references can be found in the User Manual and in the rest of the documentation found in the Docu sub directory.

There are two program versions of GLEAM and HyGLEAM per application, one command line version and one with a simple textual user interface. Both are started in a shell. There is no implementation with a graphical user interface.

Implementation Issues

The software is written in C under Linux. The distribution is based on Ubuntu 20.04.1 LTS and the GNU C and C++ compiler version 9.3.0.

Intended Use

The software is suitable

• to gain experience with EAs and Memetic Algorithms (MA),
• to implement your own optimization project based on a well-proven EA or MA, and
• to extend the MA by own heuristics or other local search methods.

Prerequisites

If you want to extend GLEAM or HyGLEAM and/or adapt it to an optimization problem, you should have

• at minimum basic knowledge about Evolutionary Algorithms,
• programming experience and experience with Linux, and
• some experience with the C programming language is certainly also helpful.

Support

The project is the result of about 30 years of research and development work at KIT, where the software was mainly developed in the context of different applications. Since the authors have retired in the meantime, there can only be little support. A further development of the project is currently not planned. Unless someone wants to take it over.

Distribution Contents

The distribution consists of

• the sources,
• make files for the generation of executables using the make utility,
• software documentation, a user guide, and a guide for included and new applications,
• some initialization files for prepared test cases, and
• some pre-compiled executables suited for linux and Ubuntu in particular.

Acronyms:

GLEAM is the abbreviation of General Learning Evolutionary Algorithm and Method and HyGLEAM stands for Hybrid GeneraL purpose Evolutionary Algorithm and Method.

Legal Issues

This is free software, which is governed by the Lesser GNU Public Licence (LGPL), version 3, see the LICENSE.md file.

Copyright (c) 2003 .. 2021 Christian Blume, Wilfried Jakob