In building practical applications of evolutionary computation (EC), two
optimizations are essential. First, the parameters of the search method need to
be tuned to the domain in order to balance exploration and exploitation
effectively. Second, the search method needs to be distributed to take
advantage of parallel computing resources. This paper presents BLADE (BLAnket
Distributed Evolution) as an approach to achieving both goals simultaneously.
BLADE uses blankets (i.e., masks on the genetic representation) to tune the
evolutionary operators during the search, and implements the search through
hub-and-spoke distribution. In the paper, (1) the blanket method is formalized
for the (1 + 1)EA case as a Markov chain process. Its effectiveness is then
demonstrated by analyzing dominant and subdominant eigenvalues of stochastic
matrices, suggesting a generalizable theory; (2) the fitness-level theory is
used to analyze the distribution method; and (3) these insights are verified
experimentally on three benchmark problems, showing that both blankets and
distribution lead to accelerated evolution. Moreover, a surprising synergy
emerges between them: When combined with distribution, the blanket approach
achieves more than n-fold speedup with n clients in some cases. The work
thus highlights the importance and potential of optimizing evolutionary
computation in practical applications