@inproceedings{YamabeKawamuraTani2004,
  author    = {Yamabe, Yuichiro and Kawamura, Hiroshi and Tani, Akinori},
  title     = {Optimal Design for Recurrent Architecture Network Harmonized with Circulation-type Societies by Applying Genetic Algorithms to Multiagent Model},
  doi       = {10.25643/bauhaus-universitaet.189},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-1892},
  year      = {2004},
  abstract  = {In this paper, a circulation-type society is expressed by recurrent architecture network described with multi-agent model which consists of the following agents: user, builder, reuse maker, fabricator, waste disposer, material maker and earth bank (see Fig.1). Structural members, materials, resources and monies move among these agents. Each agent has its own rules and aims, regarding structural damages, lifetime, cost reduction, numbers of structural members and structural systems. Reasonable prices of members (fresh, reused, recycled and disposed) can be optimized by GAs in this system considering equal distribution of monies among agents.},
  subject      = {Mehragentensystem},
  language  = {en}
}
@inproceedings{Schoof2000,
  author    = {Schoof, Jochen},
  title     = {Kommunizierende Genetische Algorithmen: Durch Evolution zur Kooperation},
  doi       = {10.25643/bauhaus-universitaet.612},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-6123},
  year      = {2000},
  abstract  = {Die Kooperation zwischen Menschen und Computern gewinnt in zahlreichen Problemstellungen mehr und mehr an Bedeutung. Ein wesentlicher Grund hierf{\"u}r ist die st{\"a}ndig wachsende Komplexit{\"a}t relevanter Problemstellungen. Dadurch bedingt sind weder der Mensch noch der Computer alleine in der Lage, zufriedenstellende L{\"o}sungen zu entwickeln. Die Kombination der individuellen F{\"a}higkeiten hat sich in vielen Bereichen als gewinnbringend erwiesen. Genetische Algorithmen (GA) als Repr{\"a}sentanten der >Evolutionary Computation< stellen einen Ansatz zur L{\"o}sung hochkomplexer Optimierungsaufgaben dar, der sich an den Vorg{\"a}ngen der Evolution orientiert. Im Gegensatz zu vielen anderen Optimierungsverfahren bringen sie einige Eigenarten mit, die kooperative Erweiterungen einfach und erfolg-versprechend machen. Der vorgestellte kommunizierende Genetische Algorithmus kombiniert die Vorteile der GA mit der F{\"a}higkeit zur Kooperation. Es gelingt bei seiner Verwendung, gute externe Vorschl{\"a}ge aufzunehmen, w{\"a}hrend schlechte Vorschl{\"a}ge keinerlei negative Auswirkungen zeigen. Diese Robustheit gegen Irrt{\"u}mer und Fehleingaben macht den KGA zu einer idealen Basis f{\"u}r Programme zur kooperativen Probleml{\"o}sung.},
  subject      = {Mensch-Maschine-Kommunikation},
  language  = {de}
}
@inproceedings{BernsteinRichter2003,
  author    = {Bernstein, Swanhild and Richter, Matthias},
  title     = {The Use of Genetic Algorithms in Finite Element Model Identification},
  doi       = {10.25643/bauhaus-universitaet.276},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-2769},
  year      = {2003},
  abstract  = {A realistic and reliable model is an important precondition for the simulation of revitalization tasks and the estimation of system properties of existing buildings. Thereby, the main focus lies on the parameter identification, the optimization strategies and the preparation of experiments. As usual structures are modeled by the finite element method. This as well as other techniques are based on idealizations and empiric material properties. Within one theory the parameters of the model should be approximated by gradually performed experiments and their analysis. This approximation method is performed by solving an optimization problem, which is usually non-convex, of high dimension and possesses a non-differentiable objective function. Therefore we use an optimization procedure based on genetic algorithms which was implemented by using the program package SLang...},
  subject      = {Finite-Elemente-Methode},
  language  = {en}
}