Online Parallel Portfolio Selection with Heterogeneous Island Model

TL;DR

This paper introduces an online parallel portfolio selection algorithm using a heterogeneous island model where different optimization algorithms run on separate islands, managed by a central planner that dynamically updates the methods based on performance, demonstrating superior versatility over homogeneous models.

Contribution

The paper proposes a novel online parallel portfolio selection algorithm with a heterogeneous island model and dynamic method management, enhancing flexibility and performance over traditional homogeneous models.

Findings

Heterogeneous models outperform homogeneous models across various problems.

Dynamic method management improves optimization efficiency.

Different representations and evaluation durations affect performance.

Abstract

We present an online parallel portfolio selection algorithm based on the island model commonly used for parallelization of evolutionary algorithms. In our case each of the islands runs a different optimization algorithm. The distributed computation is managed by a central planner which periodically changes the running methods during the execution of the algorithm -- less successful methods are removed while new instances of more successful methods are added. We compare different types of planners in the heterogeneous island model among themselves and also to the traditional homogeneous model on a wide set of problems. The tests include experiments with different representations of the individuals and different duration of fitness function evaluations. The results show that heterogeneous models are a more general and universal computational tool compared to homogeneous models.