Accelerating Evolution Through Gene Masking and Distributed Search

TL;DR

This paper introduces BLADE, a method combining gene masking and distributed search to accelerate evolutionary algorithms, with theoretical analysis and experimental validation showing significant speedups and synergy effects.

Contribution

The paper formalizes gene masking for (1+1)EA as a Markov process, analyzes distributed search with fitness-level theory, and demonstrates combined acceleration in practical benchmark problems.

Findings

Gene masking improves exploration-exploitation balance.

Distributed search significantly speeds up evolution.

Combined approach yields superlinear speedups in some cases.

Abstract

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.