Tuning Parameters of Differential Evolution: Self-adaptation, Parallel Islands, or Co-operation

Christina Brester, Ivan Ryzhikov


In this paper, we raise a question of tuning parameters of Evolutionary Algorithms (EAs) and consider three alternative approaches to tackle this problem. Since many different self-adaptive EAs have been proposed so far, it has led to another problem of choice. Self-adaptive modifications usually demonstrate different effectiveness on the set of test functions, therefore, an arbitrary choice of it may result in the poor performance. Moreover, self-adaptive EAs often have some other tuned parameters such as thresholds to switch between different types of genetic operators. On the other hand, nowadays, computing power allows testing several EAs with different settings in parallel. In this study, we show that running parallel islands of a conventional Differential Evolution (DE) algorithm with different CR and F enables us to find its variants that outperform advanced self-adaptive DEs. Finally, introducing interactions among parallel islands, i.e. exchanging the best solutions, helps to gain the higher performance, compared to the best DE island working in an isolated way. Thus, when it is hard to choose one particular self-adaptive algorithm from all existing modifications proposed so far, the co-operation of conventional EAs might be a good alternative to advanced self-adaptive EAs.


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