Evaluation of a randomized parameter setting strategy for island-model evolutionary algorithms

This paper presents a large-scale, empirical evaluation of a Random, Heterogeneous Island-Model (RHIM) for evolutionary algorithms (EAs), where the control parameter values are independently, randomly assigned for each island that has recently been proposed by Gong and Fukunaga as a method for configuring island-model evolutionary algorithms in situations where it is not possible to expend the resources to carefully tune control parameters for a particular application. We apply RHIM to standard DE, JADE (an adaptive DE), and real-coded genetic algorithms. Evaluations are performed on standard black-box function optimization benchmarks, as well as combinatorial optimization problems (the TSP and QAP). The search efficiency of RHIM is compared to manual tuning of parameter settings for each benchmark problem. Our results with up to 256 islands, show that the search efficiency of RHIM, a method which does not involve any parameter tuning, tends to becomes increasingly competitive with manual parameter tuning as the number of islands increases. The consistent, relatively good performance of RHIM when applied to a variety of EAs on numerous, different benchmark problems suggest that it can be an effective, default method for configuring island-model EAs.