How Far Are We From an Optimal, Adaptive DE?
This work addresses the challenge of improving adaptive evolutionary algorithms for optimization researchers, but it is incremental as it focuses on analysis and hints rather than a new breakthrough method.
The paper tackles the problem of understanding optimal parameter adaptation in adaptive Differential Evolution (DE) by proposing a Greedy Approximate Oracle (GAO) method to approximate it, and shows that GAO helps assess performance gaps and guide future method development through comparisons on benchmark functions.
We consider how an (almost) optimal parameter adaptation process for an adaptive DE might behave, and compare the behavior and performance of this approximately optimal process to that of existing, adaptive mechanisms for DE. An optimal parameter adaptation process is an useful notion for analyzing the parameter adaptation methods in adaptive DE as well as other adaptive evolutionary algorithms, but it cannot be known generally. Thus, we propose a Greedy Approximate Oracle method (GAO) which approximates an optimal parameter adaptation process. We compare the behavior of GAODE, a DE algorithm with GAO, to typical adaptive DEs on six benchmark functions and the BBOB benchmarks, and show that GAO can be used to (1) explore how much room for improvement there is in the performance of the adaptive DEs, and (2) obtain hints for developing future, effective parameter adaptation methods for adaptive DEs.