John Henry Holland
|Died||August 9, 2015 86) (aged|
|Alma mater||University of Michigan|
|Known for||Research on genetic algorithms|
|Awards|| MacArthur Fellow (1992)|
Harold Pender Award (1999)
Fellow of the World Economic Forum
|Institutions|| University of Michigan |
Santa Fe Institute
|Doctoral advisor||Arthur Walter Burks|
|Doctoral students||Edgar Codd Melanie Mitchell|
John Henry Holland (February 2, 1929 – August 9, 2015) was an American scientist and Professor of psychology and Professor of electrical engineering and computer science at the University of Michigan, Ann Arbor. He was a pioneer in what became known as genetic algorithms.
Holland was born in Fort Wayne, Indiana, in 1929. He studied physics at the Massachusetts Institute of Technology and received a B.S. degree in 1950, then studied Mathematics at the University of Michigan, receiving an M.A. in 1954.In 1959 he received the first computer science Ph.D. from the University of Michigan. He was a Professor of psychology and Professor of electrical engineering and computer science at the University of Michigan, Ann Arbor. He held visiting positions at the Rowland Institute for Science and the University of Bergen.
Holland was a member of the Board of Trustees and Science Board of the Santa Fe Institute and a fellow of the World Economic Forum.
Holland received the 1961 Louis E. Levy Medal from The Franklin Institute, and the MacArthur Fellowship in 1992.
He was profiled extensively in chapters 5 and 7 of the book Complexity (1993), by M. Mitchell Waldrop.
Holland died on August 9, 2015 in Ann Arbor, Michigan.
Holland frequently lectured around the world on his own research, and on research and open questions in complex adaptive systems (CAS) studies. In 1975 he wrote the ground-breaking book on genetic algorithms, "Adaptation in Natural and Artificial Systems". He also developed Holland's schema theorem.
Holland is the author of a number of books about complex adaptive systems, including:
Articles, a selection:
Complexity characterises the behaviour of a system or model whose components interact in multiple ways and follow local rules, meaning there is no reasonable higher instruction to define the various possible interactions.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. John Holland introduced genetic algorithms in 1960 based on the concept of Darwin’s theory of evolution; afterwards, his student David E. Goldberg extended GA in 1989.
In artificial intelligence (AI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.
Robert Sedgewick is an American computer science professor at Princeton University and a former member of the board of directors of Adobe Systems. Sedgewick completed his Ph.D. in 1975 under the supervision of Donald Knuth at Stanford. His thesis was about the quicksort algorithm. In 1975–85, he served on the faculty of Brown University.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
The Santa Fe Institute (SFI) is an independent, nonprofit theoretical research institute located in Santa Fe and dedicated to the multidisciplinary study of the fundamental principles of complex adaptive systems, including physical, computational, biological, and social systems. The Institute is ranked 25th among the world's "Top Science and Technology Think Tanks" and 25th among the world's "Best Transdisciplinary Research Think Tanks" according to the 2018 edition of the Global Go To Think Tank Index Reports, published annually by the University of Pennsylvania.
Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component with a learning component. Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions. This approach allows complex solution spaces to be broken up into smaller, simpler parts.
Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.
Artificial society is the specific agent based computational model for computer simulation in social analysis. It is mostly connected to the theme in complex system, emergence, Monte Carlo method, computational sociology, multi-agent system, and evolutionary programming. The concept itself is simple enough. Actually reaching this conceptual point took a while. Complex mathematical models have been, and are, common; deceivingly simple models only have their roots in the late forties, and took the advent of the microcomputer to really get up to speed.
Christopher Gale Langton is an American computer scientist and one of the founders of the field of artificial life. He coined the term in the late 1980s when he organized the first "Workshop on the Synthesis and Simulation of Living Systems" at the Los Alamos National Laboratory in 1987. Following his time at Los Alamos, Langton joined the Santa Fe Institute (SFI), to continue his research on artificial life. He left SFI in the late 1990s, and abandoned his work on artificial life, publishing no research since that time.
A complex adaptive system is a system in which a perfect understanding of the individual parts does not automatically convey a perfect understanding of the whole system's behavior. The study of complex adaptive systems, a subset of nonlinear dynamical systems, is highly interdisciplinary and blends insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents, phase transition, and emergent behavior.
J. Doyne Farmer is an American complex systems scientist and entrepreneur with interests in chaos theory, complexity and econophysics. He is a Professor of Mathematics at Oxford University, where he is Director of the Complexity Economics at the Institute for New Economic Thinking at the Oxford Martin School, and is also an external professor at the Santa Fe Institute. His current research is on complexity economics, focusing on systemic risk in financial markets and technological progress. During his career he has made important contributions to complex systems, chaos, artificial life, theoretical biology, time series forecasting and econophysics. He co-founded Prediction Company, one of the first companies to do fully automated quantitative trading. While a graduate student he led a group that called itself Eudaemonic Enterprises and built the first wearable digital computer, which was used to beat the game of roulette.
Melanie Mitchell is a professor of computer science at Portland State University. She has worked at the Santa Fe Institute and Los Alamos National Laboratory. Her major work has been in the areas of analogical reasoning, complex systems, genetic algorithms and cellular automata, and her publications in those fields are frequently cited.
David Edward Goldberg is an American computer scientist, civil engineer, and former professor. Until 2010, he was a professor in the department of Industrial and Enterprise Systems Engineering (IESE) at the University of Illinois at Urbana-Champaign and was noted for his work in the field of genetic algorithms. He was the director of the Illinois Genetic Algorithms Laboratory (IlliGAL) and the co-founder & chief scientist of Nextumi, which later changed its name to ShareThis. He is the author of Genetic Algorithms in Search, Optimization and Machine Learning, one of the most cited books in computer science.
David Pines was the founding director of the Institute for Complex Adaptive Matter (ICAM) and the International Institute for Complex Adaptive Matter (I2CAM), distinguished professor of physics, University of California, Davis, research professor of physics and professor emeritus of physics and electrical and computer engineering in the Center for Advanced Study, University of Illinois at Urbana–Champaign (UIUC), and a staff member in the office of the Materials, Physics, and Applications Division at the Los Alamos National Laboratory.
The combination of quality control and genetic algorithms led to novel solutions of complex quality control design and optimization problems. Quality control is a process by which entities review the quality of all factors involved in production. Quality is the degree to which a set of inherent characteristics fulfils a need or expectation that is stated, general implied or obligatory. Genetic algorithms are search algorithms, based on the mechanics of natural selection and natural genetics.
Scott E. Page is an American social scientist and John Seely Brown Distinguished University Professor of Complexity, Social Science, and Management at the University of Michigan, Ann Arbor, where he has been working since 2000. He has also been director of the Center for the Study of Complex Systems at the University of Michigan (2009-2014) and an external faculty member at the Santa Fe Institute.
Artificial life is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.
Stephanie Forrest is an American computer scientist and director of the Biodesign Center for Biocomputing, Security and Society at the Biodesign Institute at Arizona State University. She was previously Distinguished Professor of Computer Science at the University of New Mexico in Albuquerque. She is best known for her work in adaptive systems, including genetic algorithms, computational immunology, biological modeling, automated software repair, and computer security.
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