An architecture for massive parallelization of the compact genetic algorithm

TL;DR

This paper introduces a scalable, fault-tolerant architecture for massively parallelizing the compact genetic algorithm, potentially outperforming traditional methods and applicable to complex probabilistic models.

Contribution

It proposes a novel architecture that enables efficient, scalable parallelization of the compact genetic algorithm with low synchronization costs and fault tolerance.

Findings

Enhanced scalability and fault tolerance demonstrated

Reduced synchronization costs compared to traditional methods

Potential applicability to complex probabilistic genetic algorithms

Abstract

This paper presents an architecture which is suitable for a massive parallelization of the compact genetic algorithm. The resulting scheme has three major advantages. First, it has low synchronization costs. Second, it is fault tolerant, and third, it is scalable. The paper argues that the benefits that can be obtained with the proposed approach is potentially higher than those obtained with traditional parallel genetic algorithms. In addition, the ideas suggested in the paper may also be relevant towards parallelizing more complex probabilistic model building genetic algorithms.