Simple genetic algorithms have
been used in a wide range of engineering and scientific fields to quickly
procure useful solutions to poorly understood optimization problems.
Unfortunately, despite the routine use of these algorithms for over three
decades, their remarkable adaptive capacity has not been adequately
accounted for. In this dissertation, I develop, submit, and support the
generative fixation hypothesis—a unified explanation for the
adaptive capacity of the simple genetic algorithm.
The generative fixation hypothesis is based on the
inference of a close relationship between the simple genetic algorithm and
a promising general-purpose stochastic search heuristic, called
hyperclimbing, for optimizing over attribute product spaces (for
example, the set of all binary strings of some fixed length) with rugged
fitness functions. Hyperclimbing works by progressively limiting sampling
to a series of nested subsets with increasing expected fitness. At each step, this heuristic
searches through vast numbers of coarse
partitions of the subset it ``inhabits", and identifies ones that
partition this set into subsets whose expected fitness values are
significantly variegated. Because hyperclimbing is sensitive, not to the
local features of a search space, but to certain more global statistics,
it is not susceptible to the kinds of issues that waylay local search
The chief barrier to the wide and enthusiastic use of hyperclimbing is that it seems to scale very poorly with the number of
attributes. If one heeds the seemingly high cost of applying
hyperclimbing to large search spaces, this heuristic quickly looses its
shine. A key conclusion of this dissertation is that this seemingly high
cost is illusory. I present evidence that strongly suggests that the
algorithm can implement hyperclimbing extraordinarily efficiently.
I compare the generative fixation hypothesis with the
building block hypothesis and argue that the former surpasses the latter
on three counts:
In breaking from the building block hypothesis, the generative fixation
hypothesis reverts back to two classic positions in population genetics:
- The former hypothesis can account for the adaptive capacity of a wider
class of simple genetic algorithms. This class includes simple genetic
algorithms that use uniform crossover.
- The former hypothesis presumes less about the distribution of fitness over the chromosome set.
It does not, for example, presume the existence of a hierarchy of building
- The former hypothesis can successfully pass a demanding test of
validity, one that involves the application of a simple genetic algorithm
with and without a mechanism called clamping to large, random
instances of MAXSAT.
On a third matter, that of the unit of selection, the generative fixation
hypothesis is at odds with the position taken by orthodox neo-darwinists,
which is that the unit of selection in an evolving population is always
reducible to the unit of inheritance---that the gene, in other words, is
the ultimate unit of selection. In contrast, the generative fixation
hypothesis holds that the unit of selection can be a small set
of unlinked or weakly linked genes. This difference in positions is highly
significant from a computational standpoint.
- That fixation is the vehicle by which adaptive gains are secured.
- That the function of recombination is to prevent hitchhiking.
Jordan Pollack, Brandeis University (Committee Chair)
Harry Mairson, Brandeis University
Timothy Hickey, Brandeis University
Lee Altenberg, University of Hawaii at Manoa