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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.
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Selected Publications |
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- 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
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Software |
SpeedyGA:
A fast Simple Genetic Algorithm in Matlab
SpeedyGApy: A fast Simple Genetic Algorithm in Python
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