In spite of the featured self-adaptive mutation, implementation of Evolution Strategies ES still suffer from premature convergence. Usually, by readjusting mutation step size or easing up selective pressure, this problem can be alleviated to a certain extent. However, these solutions point to the innate deficiencies in standard ES schemes. These weaknesses include greediness of ranking-based truncation selection and a lack of feedback for mutation step adaptation. Based on previous studies, this paper attempts an alternative modification to standard ES implementation with parental population manipulation. The manipulation scheme consists of dynamic selection pooling and parental population sizing. It not only minimizes adverse interactions between above-mentioned evolution operators but also buttresses algorithm performance. Simulations on several benchmark problems vindicate the virtue of this modification.

Date Published: 21 March 2001
PDF: 11 pages
Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); doi: 10.1117/12.421181

Published in SPIE Proceedings Vol. 4390:
Applications and Science of Computational Intelligence IV
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)