Entropy-based Adaptive Range Parameter Selection for Evolutionary Algorithms

Evolutionary Algorithms are equipped with a range of adjustable parameters, such as crossover and mutation rates which significantly influence the performance of the algorithm. Practitioners usually do not have the knowledge and time to investigate the ideal parameter values before the optimisation process. Furthermore, different parameter values may be optimal for different problems, and even problem instances. In this work, we present a parameter control method which adjusts parameter values during the optimisation process using the algorithm’s performance as feedback. The approach is particularly effective with continuous parameter intervals, which are adapted dynamically. Successful parameter ranges are identified using an entropy-based clusterer, a method which outperforms state-of-the-art parameter control algorithms.