Robust Evolution Strategies

Evolution Strategies (ES) are an approach to numerical optimization that shows good optimization performance. However, it is found through our computer simulations that the performance changes with the lower bound of strategy parameters, although it has been overlooked in the ES community. We demonstrate that a population cannot practically move to other better points, because strategy parameters attain minute values at an early stage, when too small a lower bound is adopted. This difficulty is called the lower bound problem in this paper. In order to improve the self-adaptive property of strategy parameters, a new extended ES called RES is proposed. RES has redundant neutral strategy parameters and adopts new mutation mechanisms in order to utilize selectively neutral mutations so as to improve the adaptability of strategy parameters. Computer simulations of the proposed approach are conducted using several test functions.