The National Science Foundation
announced in February, 2010, that Michigan State University has been awarded one of
five new highly-coveted Science and Technology Centers, to be called
BEACON, an NSF Science & Technology
Center for the Study of Evolution in Action.
The initial award is $25 million for five years, and took effect August 1, 2010, renewable once for an additional $25 million.
BEACON
will conduct research on fundamental evolutionary dynamics in both natural
and artificial systems, educate a generation of multi-disciplinary
scientists in these methods, and will improve public understanding of
evolution at all levels. BEACON
will focus on evolution as an ongoing
process, in organisms in the laboratory (bacteria, yeast, viruses,
etc.), in the field, and with “digital organisms” undergoing evolution in
the computer. It will be
directed by Erik D. Goodman, Professor of Electrical and Computer
Engineering, and will involve more than more than thirty faculty researchers
at MSU, most in the Colleges of Engineering and Natural Science.
Four
universities and more thirty additional biologists, engineers and computers
scientists will partner with MSU in BEACON, from
The selection of five new centers by NSF took two years,
from submission of hundreds of pre-proposals in 2008, through 43 full
proposals submitted in April, 2009, and NSF site visits to eleven finalists
last fall. The BEACON team,
nearly 70 investigators, collaborated to generate the plans for the center.
As the lead institution, MSU made major commitments of space and
other resources to BEACON, and the partner universities have also pledged
extensive support. BEACON will
be headquartered in MSU’s Biomedical Physical Sciences Building, in space
being remodeled for use by BEACON and iCER, the Institute for Cyber-Enabled
Research, for which BEACON will likely be the biggest “customer.”
As Goodman, a long-time researcher and practitioner of engineering
applications of evolutionary computation, says, “BEACON is multidisciplinary
to its core, and in addition to making discoveries in basic science and
applications, will prepare a new generation of researchers with the insight
that comes from first-hand experimentation with evolution in the lab and in
the computer. Recognizing the
commonality of evolutionary dynamics in both contexts will enable studies
and applications that could not be done in isolation in either biology or
engineering.” The backgrounds
of the four co-principal investigators in BEACON reinforce its
multidisciplinary character.
Richard Lenski, Hannah Professor of Microbiology and Molecular Genetics and
member of the National Academy of Sciences, has been studying evolution of
E. coli bacteria for twenty years
(50,000 generations), with regular freezing of samples allowing him to trace
mutations backward in time to pinpoint when they occurred and to explore
alternative paths from ancestors.
Charles Ofria, Associate Professor of Computer Science and
Engineering, is the author of the Avida software system for evolution of
“digital organisms” in the computer.
His digital organisms self-replicate in the computer, producing
(sometimes mutated) copies of themselves and competing for resources.
Kay Holekamp, Professor of Zoology and director of the
interdisciplinary program in Ecology, Evolutionary Biology and Behavior,
studies evolution of behavior and morphology among spotted hyenas in Kenya.
Robert Pennock, Professor of Philosophy and in Lyman Briggs College,
studies evolution and leads a team creating software to allow students to
experiment with digital evolution.
MSU learned in August, 2009, that a joint proposal from Goodman (College of Engineering), Johannes Bauer and Kurt DeMaagd, of the College of Communication Arts and Sciences, will be funded at about $600,000 over three years. The project, under NSF�s Cyber-Enabled Discovery and Innovation Program, will use agent-based modeling of the telecommunications infrastructure to first develop a model that seeks to reflect adequately many aspects of the market- and regulation-driven dynamics of the telecommunications industry. Evolutionary computation will be used in two ways: 1) in this first stage, to help to find the best structure and parameters to capture the dynamics of this complex system, and 2) in the subsequent stage, to search the space of possible regulatory mechanisms, seeking those that produce the DESIRED emergent properties of this complex system. In the past, many regulatory actions have, in fact, produced effects almost directly opposite to those desired. The goal here is to capture enough of the underlying relationships among the many agents (companies, agencies, market sectors, etc.) that the results of a particular regulatory change can be gauged well enough to allow search for good combinations of regulations to move the infrastructure in desired directions. Goodman will provide expertise in evolutionary computation, and Bauer and DeMaagd are experts in telecommunications policy and modeling of the telecommunications infrastructure.
My principal personal research interest is evolutionary computation, and in particular, parallel genetic algorithms and genetic programming.
My work with Dr. Ron Averill and our students in applying genetic algorithms for automotive structural design constituted a breakthrough in automating the design of structures for crashworthiness, noise, vibration, harshness, manufacturability, etc. As a result of our demonstrated success, we organized a company, Red Cedar Technology, Inc. (originally called Applied Computational Design Associates, Inc., or "ACD Associates"), which offers design services using our various GA and FEA technologies to the industrial community. Current customers come from automotive, marine, aerospace, medical instrument and appliance, manufacturing equipment, architecture, and civil infrastructure industries. The company is simultaneously developing software products and training to make its tools accessible to industry. The company's web page is Red Cedar Technology. Ron Averill is President, and I am Vice President and Chief Technology Officer.
Under a grant from the National Science Foundation, my co-investigator, Ron Rosenberg, research associate, Dr. Kisung Seo, and I worked with a team of outstanding students, including Jianjun Hu, Zhun Fan, and Janelle Shane, on using genetic programming for automated design of multi-domain systems (electrical, mechanical, etc.), including mechatronics. The output of the GPBG system is a bond graph specifying the connection topology and components, including values of parameters, to implement a system with a given desired performance. For more information about GPBG, please see these pages. During the development of GPBG, Jianjun Hu (now at Univ. of South Carolina) created the Hierarchical Fair Competition principle, which has been shown to facilitate rapid, sustainable search in many domains of evolutionary computation, including GP and GA. For more information, see the HFC pages.
In the early 1990's, I wrote (in 'C') a package called GALOPPS, which is distributed via the net. It includes capabilities for such innovative PGA architectures as the "Injection Island GA" or iiGA, which was developed in the GARAGe, in which a hierarchy of populations, using different problem representations and/or different fitness functions and/or different local search heuristics, migrates solutions to populations using increasingly more accurate problem representations or fitness functions. My student Wang, Gang also released DAGA2, a 2-level hierarchical and parallel GA which chooses GA operators/rates, etc., using a second level of adaptation (evaluating fitness of subpopulations in moving toward problem solution), and is "plug-compatible" with GALOPPS, for persons already using GALOPPS for simple or parallel GA work. For information regarding our GA research, please see the GARAGe web page. I am very interested now in how to best communicate information among various subpopulations simultaneously working on a problem, often with each subpopulation using a somewhat different representation of the problem or a different fitness function for evaluating solutions.
I have worked with genetic algorithms for 34 years. I believe that my Ph.D. research, in 1970-71, was the first time a GA was used to solve a real problem (not just a test or benchmark problem). In 1970, after taking two courses in what is now called evolutionary computation, from John Holland, I began a run of a GA (which took more than a year to complete, in a checkpoint/restart configuration, running over half the time) using a floating-point-representation GA, with Gaussian mutation of floating point variables, as part of my Ph.D. research in the Logic of Computers Group at the University of Michigan (continued on a computer at Michigan State University after my hiring there in September, 1971). I can't cite a publication on the GA methods because I couldn't manage to get it published at the time -- it was seen as pretty strange, then. However, the 40 GA-determined rate parameters in my publications about the E. coli model were the outputs of the GA. I used a GA in my EPA-sponsored modeling work in the 70's, with a Ph.D. student, Mehrdad Tabatabaai. My Ph.D. student Adrian Sannier and I used a GA in what would now be called "linear genetic programming" to evolve programs governing artificial organisms, in a primitive form of A-life. We were able to evolve two species of cooperating organisms, and eventually, a combined organism that differentiated based on its early experiences in the environment. This work was published in the Second International Conference on Genetic Algorithms (1987) and related work appeared in other places.
From 1993-2003, I directed MSU's Manufacturing Research Consortium, which conducted research at MSU under sponsorship of industrial members, under two sequential 5-year agreements.
I have also conducted research in environmental modeling and simulation since 1972. In 1995, our Environmentally Responsible Manufacturing (ERM) team at MSU received a grant from NSF to develop tools enabling manufacturing enterprises to incorporate environmental tradeoff information directly into their existing management tools, rather than using it later in a "checkoff" process. The MSU Manufacturing Research Consortium also sponsored a related project on the "Green Supply Chain."
