Understanding Simple Asynchronous Evolutionary Algorithms

Abstract

In many applications of evolutionary algorithms, the time required to evaluate the fitness of individuals is long and variable. When the variance in individual evaluation times is non-negligible, traditional, synchronous master-slave EAs incur idle time in CPU resources. An asynchronous approach to parallelization of EAs promises to eliminate idle time and thereby to reduce the amount of wall-clock time it takes to solve a problem. However, the behavior of asynchronous evolutionary algorithms is not well understood. In particular, it is not clear exactly how much faster the asynchronous algorithm will tend to run, or whether its evolutionary trajectory may follow a sub-optimal search path that cancels out the promised benefits. This paper presents a preliminary analysis of simple asynchronous EA performance in terms of speed and problem-solving ability.