Applying Evolutionary Algorithms Successfully: A Guide Gained from Real-world Applications

Metaheuristics (MHs) in general and Evolutionary Algorithms (EAs) in particular are well known tools for successful optimization of difficult problems. But when is their application meaningful and how does one approach such a project as a novice? How do you avoid beginner\u27s mistakes or use the design possibilities of a metaheuristic search as efficiently as possible? This paper tries to give answers to these questions based on 30 years of research and application of the Evolutionary Algorithm GLEAM and its memetic extension HyGLEAM. Most of the experience gathered and discussed here can also be applied to the use of other metaheuristics such as ant algorithms or particle swarm optimization. This paper addresses users with basic knowledge of MHs in general and EAs in particular who want to apply them in an optimization project. For this purpose, a number of questions that arise in the course of such a project are addressed. At the end, some non-technical project management issues are discussed, whose importance for project success is often underestimated

Optimised planning and scheduling of grid resources

This article will present the concept and implementation of a resource management system (RMS). The central component of the RMS is the resource broker GORBA that plans resource allocation by a combination of heuristic processes and evolutionary algorithms. For resource planning, schedules are generated, which distribute the grid jobs to the grid resources in a defined time window. A test environment with extensive visualisation options was developed for GORBA, which will be presented in detail. Using this test environment, benchmark runs were carried out, which are needed to evaluate and further develop GORBA. Automated resource planning and the graphic visualisation options facilitate the usability of a grid environment

Optimised planning and scheduling of grid resources

This article will present the concept and implementation of a resource management system (RMS). The central component of the RMS is the resource broker GORBA that plans resource allocation by a combination of heuristic processes and evolutionary algorithms. For resource planning, schedules are generated, which distribute the grid jobs to the grid resources in a defined time window. A test environment with extensive visualisation options was developed for GORBA, which will be presented in detail. Using this test environment, benchmark runs were carried out, which are needed to evaluate and further develop GORBA. Automated resource planning and the graphic visualisation options facilitate the usability of a grid environment

GLEAM - General Learning Evolutionary Algorithm and Method : ein Evolutionärer Algorithmus und seine Anwendungen

Nach einer grundlegenden Einführung wird der Evolutionäre Algorithmus GLEAM ausführlich vorgestellt. Das breite Anwendungspotential dieses Optimierungs- und Planungsverfahrens wird durch eine Reihe von Anwendungsbeispielen aus den Bereichen Robotik, Scheduling, Bauindustrie und Designoptimierung unterstrichen. Dabei werden auch Weiterentwicklungen behandelt, die Heuristiken und lokale Suche in das evolutionäre Verfahren integrieren, so dass ein hybrider oder memetischer Algorithmus entsteht

Proceedings. 16. Workshop Computational Intelligence, Dortmund, 29. Nov.-1. Dez. 2006

These proceedings contain the papers of the 16th Workshop Computational Intelligence. It was organized by the Working Group 5.14 of the VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) and the Working Group Fuzzy-Systems and Soft-Computing of the Gesellschaft für Informatik (GI)