Research

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Keki Burjorjee



Blog: Evorithmics
Ph.D. Thesis: Generative Fixation
 

Research

 
Over 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 noisy 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.

The generative fixation hypothesis is a novel account of optimization in genetic algorithms. This hypothesis proceeds from evidence that genetic algorithms with uniform crossover can implement a general-purpose, noise-tolerant global optimization 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.


Selected Publications


  • Hypomixability Elimination in Evolutionary Systems [pdf]
    Keki M. Burjorjee
    Foundations of Genetic Algorithms Conference, 2015
     

  • Explaining Optimization in Genetic Algorithms with Uniform Crossover, [pdf]  [blog post]
    Keki M. Burjorjee
    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
    Foundations of Genetic Algorithms Conference, 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
    Genetic and Evolutionary Computation Conference, 2006
     
  • Theme Preservation and the Evolution of Representation, [pdf]
    Keki M. Burjorjee, Jordan B. Pollack
    Proceedings of the 2nd Indian International Conference on Artificial Intelligence, 2005
     

Software


SpeedyGA: A fast Simple Genetic Algorithm in Matlab
SpeedyGApy: A fast Simple Genetic Algorithm in Python