Adaptive Population Models for Offspring Populations and Parallel Evolutionary Algorithms

TL;DR

This paper introduces two adaptive, parameterless schemes for dynamically adjusting the number of parallel instances in evolutionary algorithms, optimizing speed-ups without prior problem knowledge.

Contribution

The paper proposes novel adaptive schemes for parallel evolutionary algorithms that automatically adjust the number of instances, improving efficiency in black-box settings.

Findings

Both schemes achieve near-optimal speed-ups in parallel time.

Upper bounds on total function evaluations are comparable to non-parallel algorithms.

Schemes adaptively double or halve instances based on success, without prior parameter tuning.

Abstract

We present two adaptive schemes for dynamically choosing the number of parallel instances in parallel evolutionary algorithms. This includes the choice of the offspring population size in a (1+$\lambda$) EA as a special case. Our schemes are parameterless and they work in a black-box setting where no knowledge on the problem is available. Both schemes double the number of instances in case a generation ends without finding an improvement. In a successful generation, the first scheme resets the system to one instance, while the second scheme halves the number of instances. Both schemes provide near-optimal speed-ups in terms of the parallel time. We give upper bounds for the asymptotic sequential time (i.e., the total number of function evaluations) that are not larger than upper bounds for a corresponding non-parallel algorithm derived by the fitness-level method.