Wolfgang Banzhaf
Entomologist and Osteopathic Medical Specialist | Michigan State University
East Lansing, MI, UNITED STATES
Expert in genetic programming and evolutionary computation
Media
Publications:
Documents:
Photos:
Videos:
Audio/Podcasts:
Biography
Wolfgang Banzhaf is the John R. Koza Chair for Genetic Programming in the Department of Computer Science and Engineering at Michigan State University. Previously, he was a university research professor in the Department of Computer Science Memorial University of Newfoundland He served as head of department there from 2003 to 2009 and from 2012 to 2016.
Prof. Banzhaf received a "Diplom in Physik" degree in Physics (equivalent to a M.Sc.) from the Ludwig-Maximilians-University in Munich. He received his Dr.rer.nat (PhD) from the Department of Physics of the Technische Hochschule Karlsruhe, now Karlsruhe Institute of Technology (KIT). Prof. Banzhaf was postdoctoral research associate at the 1. Institute of Theoretical Physics of the University of Stuttgart, Visiting and Senior Researcher at the Central Research Lab, now the Advanced Technology R&D Center of Mitsubishi Electric Corporation in Japan and at Mitsubishi Electric Research Labs (MERL) in Cambridge, MA, USA. From 1993 to 2003 he was Associate Professor for Applied Computer Science in the Department of Computer Science of the Technical University of Dortmund.
Prof. Banzhaf's research interests are in the field of bio-inspired computing, notably evolutionary computation and complex adaptive systems. Studies of self-organization and the field of Artificial Life are also of very much interest to him. Recently he has become more involved with network research as it applies to natural and man-made systems.
Prof. Banzhaf is a Senior Fellow of the former International Society for Genetic and Evolutionary Computation (ISGEC) and has received the EvoStar Award for Outstanding Achievements in Evolutionary Computation in Europe. In 2010 he has been appointed University Research Professor at Memorial University for sustained contributions to research, the highest honour Memorial University bestows on an academic.
Industry Expertise (4)
Education/Learning
Computer Software
Research
Biotechnology
Areas of Expertise (6)
Computer Science
Evolutionary Computation
Machine Learning
Algorithms
Theoretical Physics
Artificial Life
Accomplishments (1)
Outstanding Achievements in Evolutionary Computation in Europe (professional)
From EvoStar Awards
Education (2)
The Technische Hochschule Karlsruhe (now Karlsruhe Institute of Technology [KIT]): Dr.rer.nat (Ph.D.), Physics
Ludwig-Maximilians-University: M.S., Physics
Affiliations (1)
- Executive Board of the International Society for Artificial Life (ISAL)
Links (2)
News (2)
$10m Gift to MSU will Fuel Genetics Effort
WKAR
2016-12-12
Current State learns more about the gift, the BEACON center and genetic programming from director Erik Goodman and professor Wolfgang Banzhaf, the BEACON Center’s first endowed chair, made possible by a previous gift from Mr Koza for $2-million...
MSU Hires Nation's First Endowed Chair in Genetic Programming
MSU Today
2015-06-08
Wolfgang Banzhaf, currently with Memorial University of Newfoundland, will be the first to hold the John R. Koza Endowed Chair in Genetic Programming. Banzhaf will join MSU in August 2016. “Dr. Banzhaf is among the most renowned computer scientists in the world studying genetic programming,” said Erik Goodman, director of MSU’s BEACON Center for the Study of Evolution in Action. “We are quite pleased he accepted the position, and are fortunate to be able to fund the position because of a generous gift from John Koza, a pioneer of the field of genetic programming.”...
Journal Articles (5)
Defining and simulating open-ended novelty: requirements, guidelines, and challenges
Theory in Biosciences2016 The open-endedness of a system is often defined as a continual production of novelty. Here we pin down this concept more fully by defining several types of novelty that a system may exhibit, classified as variation, innovation, and emergence. We then provide a meta-model for including levels of structure in a system’s model. From there, we define an architecture suitable for building simulations of open-ended novelty-generating systems and discuss how previously proposed systems fit into this framework. We discuss the design principles applicable to those systems and close with some challenges for the community.
Quantitative analysis of evolvability using vertex centralities in phenotype network
Proceedings of the Genetic and Evolutionary Computation Conference 20162016 In an evolutionary system, robustness describes the resilience to mutational and environmental changes, whereas evolvability captures the capability of generating novel and adaptive phenotypes. The research literature has not seen an effective quantification of phenotypic evolvability able to predict the evolutionary potential of the search for novel phenotypes. In this study, we propose to characterize the mutational potential among different phenotypes using the phenotype network, where vertices are phenotypes and edges represent mutational connections between them. In the framework of such a network, we quantitatively analyze the evolvability of phenotypes by exploring a number of vertex centrality measures commonly used in complex networks. In our simulation studies we use a Linear Genetic Programming system and a population of random walkers. Our results suggest that the weighted eigenvector centrality serves as the best estimator of phenotypic evolvability.
Open-ended evolution: Perspectives from the OEE workshop in York'
Artificial Life2016 We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE—it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016.
The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing2010 Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information, fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will encompass core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.
A comparison of linear genetic programming and neural networks in medical data mining
IEEE Transactions on Evolutionary Computation2001 We introduce a new form of linear genetic programming (GP). Two methods of acceleration of our GP approach are discussed: 1) an efficient algorithm that eliminates intron code and 2) a demetic approach to virtually parallelize the system on a single processor. Acceleration of runtime is especially important when operating with complex data sets, because they are occurring in real-world applications. We compare GP performance on medical classification problems from a benchmark database with results obtained by neural networks. Our results show that GP performs comparably in classification and generalization.
