World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
15718
World Ranking
7005
National Ranking
3069

Overview

John J. Grefenstette is affiliated with the University of Pittsburgh in the United States. Their research spans several fields, with a primary focus on mathematics and its applications in epidemiology and related areas.

Their scholarly contributions include publications in notable venues such as PLoS Computational Biology, Frontiers in Artificial Intelligence, and the Journal of Applied Research on Children Informing Policy for Children at Risk. The recent papers authored or co-authored by John J. Grefenstette include:

  • Detecting critical slowing down in high-dimensional epidemiological systems, 2020, PLoS Computational Biology
  • Planning as Inference in Epidemiological Dynamics Models, 2022, Frontiers in Artificial Intelligence
  • Interventions in measles outbreaks: the potential reduction in cases associated with school suspension and vaccination interventions, 2020, Journal of Applied Research on Children Informing Policy for Children at Risk

Their work covers multiple main topics, reflecting interdisciplinary interests in epidemiology, ecology, and public health. These topics include:

  • COVID-19 epidemiological studies
  • Ecosystem dynamics and resilience
  • Mental Health Research Topics
  • Climate change impacts on agriculture
  • Complex Systems and Decision Making
  • Vaccine Coverage and Hesitancy
  • Virology and Viral Diseases

Within the broader field of mathematics, John J. Grefenstette's subfields include Modeling and Simulation, Global and Planetary Change, Experimental and Cognitive Psychology, Ecology, Evolution, Behavior and Systematics, and Management Science and Operations Research.

Frequent collaborators in their research have included Mary G. Krauland, Tobias Brett, Marco Ajelli, Quan-Hui Liu, and Willem G. van Panhuis. These partnerships suggest a collaborative approach to investigating epidemiological dynamics and related systems.

Their publications often address complex challenges in modeling epidemiological processes and responses to infectious diseases, emphasizing computational and inferential techniques. Given the topics and venues where they publish, their work contributes to advancing understanding in epidemiological modeling, disease intervention strategies, and broader ecological and mental health impacts.

Best Publications

  • Optimization of Control Parameters for Genetic Algorithms

    John J. Grefenstette

  • Genetic Algorithms for the Traveling Salesman Problem

    John J. Grefenstette;Rajeev Gopal;Brian J. Rosmaita;Dirk Van Gucht

  • Genetic algorithms for changing environments

    John J. Grefenstette

  • A systematic review of barriers to data sharing in public health

    Willem G van Panhuis;Proma Paul;Claudia Emerson;John Grefenstette

  • Credit assignment in rule discovery systems based on genetic algorithms

    John J. Grefenstette

  • Genetic Algorithms for Tracking Changing Environments

    Helen G. Cobb;John J. Grefenstette

  • Genetic Algorithms in Noisy Environments

    J. Michael Fitzpatrick;John J. Grefenstette

  • Evolutionary algorithms for reinforcement learning

    David E. Moriarty;Alan C. Schultz;John J. Grefenstette

  • Learning Sequential Decision Rules Using Simulation Models and Competition

    John J. Grefenstette;Connie Loggia Ramsey;Alan C. Schultz

  • A parallel genetic algorithm

    Chrisila B. Pettey;Michael R. Leuze;John J. Grefenstette

  • Deception Considered Harmful.

    John J. Grefenstette

  • Case-Based Initialization of Genetic Algorithms

    Connie Loggia Ramsey;John J. Grefenstette

  • FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations

    John J Grefenstette;Shawn T Brown;Roni Rosenfeld;Jay DePasse

  • A Coevolutionary Approach to Learning Sequential Decision Rules

    Mitchell A. Potter;Kenneth A. De Jong;John J. Grefenstette

  • Genetic Search with Approximate Function Evaluation

    John J. Grefenstette;J. Michael Fitzpatrick

  • Evolvability in dynamic fitness landscapes: a genetic algorithm approach

    J.J. Grefenstette

  • Genetic Algorithms in Noisy Environments

    Unknown

  • A computer simulation of vaccine prioritization, allocation, and rationing during the 2009 H1N1 influenza pandemic.

    Bruce Y. Lee;Shawn T. Brown;Shawn T. Brown;George W. Korch;Philip C. Cooley

  • Lamarckian Learning in Multi-Agent Environments.

    John J. Grefenstette

  • Multi-objective learning via genetic algorithms

    J. David Schaffer;John J. Grefenstette

  • Genetic algorithms and machine learning

    Unknown

  • Genetic algorithms and their applications

    John J. Grefenstette

  • Proceedings of the First International Conference on Genetic Algorithms and their Applications

    John J. Grefenstette

  • parallel genetic algorithm

    C.B. Pettey;M.R. Leuze;J.J. Grefenstette

Frequent Co-Authors

Donald S. Burke
Donald S. Burke University of Pittsburgh
Alan C. Schultz
Alan C. Schultz United States Naval Research Laboratory
Richard K. Zimmerman
Richard K. Zimmerman University of Pittsburgh
Curtis P. Van Tassell
Curtis P. Van Tassell Agricultural Research Service
Roni Rosenfeld
Roni Rosenfeld Carnegie Mellon University
J. Michael Fitzpatrick
J. Michael Fitzpatrick Vanderbilt University
Kenneth de Jong
Kenneth de Jong George Mason University
Jessica G. Burke
Jessica G. Burke University of Pittsburgh
John L. Williams
John L. Williams University of Adelaide
William Barendse
William Barendse Commonwealth Scientific and Industrial Research Organisation

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