Haym Hirsh's Publications:
Applications in Engineering Design

Some papers can be viewed by clicking on their titles.
(A few more still need to be put on-line.)

• Mark Schwabacher, Tom Ellman, and Haym Hirsh (2001).
  Learning to Set Up Numerical Optimizations of Engineering Designs.
  In Dan Braha, editor, Data Mining for Design and Manufacturing: Methods and Applications.
  Kluwer Academic Publishers.
• Khaled Rasheed and Haym Hirsh (2000).
  Informed Operators: Speeding Up Genetic-Algorithm-Based Design Optimization Using Reduced Models.
  Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2000).
  
• Khaled Rasheed and Haym Hirsh (1999).
  Learning to be Selective in Genetic-Algorithm-Based Design Optimization.
  Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
• Mark Schwabacher, Tom Ellman, and Haym Hirsh (1998).
  Learning to Set Up Numerical Optimizations of Engineering Designs.
  Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 12(2):173-192.
• Khaled Rasheed, Haym Hirsh, and Andrew Gelsey (1997).
  A Genetic Algorithm for Continuous Design Space Search.
  Artificial Intelligence in Engineering, 11(3):295-305.
• Khaled Rasheed and Haym Hirsh (1997).
  Using Case-Based Learning to Improve Genetic-Algorithm-Based Design Optimization.
  Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA97).
• Khaled Rasheed and Haym Hirsh (1997).
  Guided Crossover: A New Operator for Genetic Algorithm Based Optimization.
  Rutgers University Computer Science Department Technical Report HPCD-TR-50.
• Mark Schwabacher, Thomas Ellman, Haym Hirsh, and Gerard Richter (1996).
  Learning to Choose a Reformulation for Numerical Optimization of Engineering Designs.
  Proceedings of the Artificial Intelligence in Design Conference (AID96).
• Mark Schwabacher, Thomas Ellman, and Haym Hirsh (1996).
  Inductive Learning for Engineering Design Optimization. (Research abstract).
  Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM), 10:179-180.
• Mark Schwabacher, Haym Hirsh, and Tom Ellman (1996).
  Learning To Select Prototypes and Reformulations for Design.
  Working Notes of the Workshop on Machine Learning in Design.
  Artificial Intelligence in Design Conference (AID96).
• Mark Schwabacher, Tom Ellman, Haym Hirsh, and Gerard Richter (1995).
  Learning When Reformulation is Appropriate for Iterative Design.
  Working Notes of the Workshop on Machine Learning in Engineering.
  Fourteenth International Joint Conference on Artificial Intelligence (IJCAI95).
  (Also Rutgers University Computer Science Department Technical Report HPCD-TR-28.)
• Mark Schwabacher, Haym Hirsh, and Thomas Ellman (1995).
  Inductive Learning for Engineering Design Optimization.
  Working Notes of the Workshop on Applying Machine Learning in Practice.
  Fourteenth International Joint Conference on Artificial Intelligence (IJCAI95).
• Mark Schwabacher, Tom Ellman, Haym Hirsh, and Gerard Richter (1995).
  Learning When Reformulation is Appropriate for Iterative Design.
  Working Notes of the Symposium on Abstraction, Reformulation, and Approximation.
• Mark Schwabacher, Haym Hirsh, and Thomas Ellman (1994).
  Inductive Learning of Prototype Selection Rules for Case-Based Iterative Design.
  Proceedings of the Tenth IEEE Conference on Artificial Intelligence for Applications (CAIA94), pages 56-62.
  IEEE Computer Society Press.
• Mark Schwabacher, Haym Hirsh, and Tom Ellman (1993).
  Inductive Learning of Prototype-Selection Rules for Case-Based Iterative Design.
  Working Notes of the Workshop on Artificial Intelligence in Design.
  Thirteenth International Joint Conference on Artificial Intelligence (IJCAI93).