In BEACON, Goodman Guides and Collaborates on Many Evolutionary Computation Research Projects
In addition to his duties as BEACON's Director and PI, Goodman engages in guiding of research of graduate students and visiting faculty members. He often co-advises with other faculty including Professors Deb, Banzhaf, Averill, Deller and Xu. Some of the projects currently underway include:
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 in 2010 was $25 million for five years, and took effect August 1, 2010. BEACON was renewed in 2015, for an additional $22.5 million, carrying its NSF funding as an STC through 2020.
conducts research on fundamental evolutionary dynamics in both natural
and artificial systems, educates a generation of multi-disciplinary
scientists in these methods, and works to improve public understanding of
evolution at all levels. BEACON
focuses 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 is
directed by Erik D. Goodman, Professor of Electrical and Computer
Engineering, and in 2016, involved more than 600 researchers at all
BEACON members are at MSU, most in the Colleges of Engineering and Natural Science, and at its four partner schools, North Carolina A&T State University, University of Idaho, University of Texas Austin and University of Washington..
BEACON unites biologists who study natural evolutionary processes
with computer scientists and engineers who harness these processes to solve
real-world problems. Developers
of so-called evolutionary algorithms
have long borrowed high-level concepts from biology to improve
problem-solving methods, but have not always captured the nuances of natural
evolution. Close collaboration
with biologists studying evolution will promote better modeling and
harnessing of the process.
Similarly, studying the evolution of artificial systems in the computer can
provide biologists insight into factors that influence the dynamics of
evolution. BEACON will promote the transfer of discoveries from biology into
computer science and engineering design, while using novel computational
methods and systems to address complex biological questions that are
difficult or impossible to study with natural organisms. BEACON's reviewers have uniformly declared the center to be a great success...
more information about BEACON's achievements to date, visit
My principal research interest has long been evolutionary computation, since beginning in the field during my thesis research. I am particularly interested in parallel genetic algorithms, genetic programming, and multi-objective evolutionary optimization.
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 has simultaneously developed software products and training to make its tools accessible to industry. The company's web page is Red Cedar Technology. During its founding years, Ron Averill was President, and I was Vice President and Chief Technology Officer. In 2013, Red Cedar became a wholly-owned subsidiary of CD Adapco, a leading provider of computational fluid dynamics (CFD) software. In 2016, CD Adapco and Red Cedar were acquired by Siemens GMBH.
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."