Research

   Publications  

   Awards & Honors

   Software

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Keki Burjorjee



Ph.D. Thesis: Generative Fixation
Blog: Hacking Evolution

Phone: (617) 645-6582
Email: kekib (at) cs.brandeis.edu
 

Research

 
Over several decades of use in diverse scientific and engineering fields, evolutionary optimization has acquired a reputation for being a kind of universal acid—a general purpose approach that routinely procures useful solutions to optimization problems with rugged, dynamic, and stochastic cost functions over search spaces consisting of strings, vectors, trees, and instances of other kinds of data structures. Remarkably, despite years of research, the means by which evolutionary algorithms work is not well understood.

I've developed a new explanation for adaptation in genetic algorithms. This account— the generative fixation hypothesis—is based on evidence that genetic algorithms can implement a stochastic non-local search heuristic, called hyperclimbing, extraordinarily efficiently. The generative fixation hypothesis departs from the reigning hypothesis in genetic algorithmics—the building block hypothesis—at a fundamental level. The potential impact of this new hypothesis extends to the fields of evolutionary computation, optimization, machine learning, theoretical computer science, and evolutionary biology.


Publications & Manuscripts

 
  • Explaining Adaptation in Genetic Algorithms with Uniform Crossover, [pdf]
    Keki M. Burjorjee
    To appear in Foundations of Genetic Algorithms Conference 2013
     

  • Generative Fixation: A Unified Explanation for the Adaptive Capacity of Simple Recombinative Genetic Algorithms [website]
    Keki M. Burjorjee
    Ph.D. Thesis, Brandeis University, August 2009
     
  • Sufficient Conditions for Coarse-Graining Evolutionary Dynamics, [pdf], slides [pdf]
    Keki M. Burjorjee
    In Foundations of Genetic Algorithms Conference IX, 2007
     
  •  A General Coarse-Graining Framework for Studying Simultaneous Inter-Population Constraints Induced by Evolutionary Operations, [pdf], extended version with proofs [pdf], slides [pdf]
    Keki M. Burjorjee, Jordan B. Pollack
    In Proceedings of the 2006 Genetic and Evolutionary Computation Conference
     
  • Theme Preservation and the Evolution of Representation, [pdf]
    Keki M. Burjorjee, Jordan B. Pollack
    In Proceedings of the 2nd Indian International Conference on Artificial Intelligence, 2005
     
  • Theme Preservation and the Evolution of Representation, [pdf]
    Keki M. Burjorjee, Jordan B. Pollack
    In Proceedings of the Theory of Representation Workshop at the Genetics and Evolutionary Computation Conference, 2005
     

Awards & Honors

 
  • Winner of the Tiny GA competition (GECCO 2006)
  • Awarded the Sproull Fellowship for "unusually strong potential for graduate study" (University of Rochester, 2000)
     
  • Awarded the Mary Evelyn Wells and Gertrude Smith Prize for excellence in the study of undergraduate Mathematics (Vassar College, 1998)
     
  • General honors, and departmental honors in mathematics, and computer science (Vassar College, 1998)
     
  • Part of a team of three that placed second in a national high school programming competition (Computer Society of India, 1992)

Software


SpeedyGA: A fast Simple Genetic Algorithm in Matlab.