Design und Management komplexer technischer Prozesse und Systeme mit Methoden der Computational Intelligence Evolutionary Optimization of Dynamic Multiobjective Functions

Design und Management komplexer technischer Prozesse und Systeme mit Methoden der Computational Intelligence Pareto Set and EMOA Bahavior for Simple Multimodal Multiobjective Functions

Recent research on evolutionary multiobjective optimization has mainly focused on Pareto-fronts. However, we state that proper behavior of the utilized algorithms in decision/search space is necessary for obtaining good results if multimodal objective functions are concerned. Therefore, it makes sense to observe the development of Pareto-sets as well. We do so on a simple, configurable problem,...

Design und Management komplexer technischer Prozesse und Systeme mit Methoden der Computational Intelligence Pareto Set and EMOA Behavior for Simple Multimodal Multiobjective Functions

Recent research on evolutionary multiobjective optimization has mainly focused on Pareto-fronts. However, we state that proper behavior of the utilized algorithms in decision/search space is necessary for obtaining good results if multimodal objective functions are concerned. Therefore, it makes sense to observe the development of Pareto-sets as well. We do so on a simple, configurable problem,...

Sonderforschungsbereich 531: Design und Management komplexer Prozesse und Systeme mit Methoden der Computational Intelligence

Design und Management komplexer technischer Prozesse und Systeme mit Methoden der Computational Intelligence TAKEOVER TIME IN PARALLEL POPULATIONS WITH MIGRATION

The term takeover time regarding selection methods used in evolutionary algorithms denotes the (expected) number of iterations of the selection method until the entire population consists of copies of the best individual, provided that the initial population consists of a single copy of the best individual whereas the remaining individuals are worse. Here, this notion is extended to parallel su...

Design und Management komplexer technischer Prozesse und Systeme mit Methoden der Computational Intelligence On The Effect of Populations in Evolutionary Multi-objective Optimization

Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. Most studies of MOEAs are empirical. Only recently, a few theoretical results have appeared. It is acknowledged that more theoretical research is needed. An important open problem is to understand the role of populations in MOEAs. We present a simple bi-objective probl...