A Feature-Based Prediction Model of Algorithm Selection for Constrained Continuous Optimisation
This provides incremental improvement in algorithm selection for researchers and practitioners in constrained continuous optimization.
The paper tackles the problem of selecting the best bio-inspired optimization algorithm for constrained continuous optimization problems by using evolved problem instances to build prediction models. Their multi-objective approach combined with random instances achieved accurate algorithm selection across a wide range of problems.
With this paper, we contribute to the growing research area of feature-based analysis of bio-inspired computing. In this research area, problem instances are classified according to different features of the underlying problem in terms of their difficulty of being solved by a particular algorithm. We investigate the impact of different sets of evolved instances for building prediction models in the area of algorithm selection. Building on the work of Poursoltan and Neumann [11,10], we consider how evolved instances can be used to predict the best performing algorithm for constrained continuous optimisation from a set of bio-inspired computing methods, namely high performing variants of differential evolution, particle swarm optimization, and evolution strategies. Our experimental results show that instances evolved with a multi-objective approach in combination with random instances of the underlying problem allow to build a model that accurately predicts the best performing algorithm for a wide range of problem instances.