NDLI: Is MO-CMA-ES superior to NSGA-II for the evolution of multi-objective neuro-controllers?

Content Provider	IEEE Xplore Digital Library
Author	Moshaiov, A. Abramovich, O.
Copyright Year	2014
Description	Author affiliation: Sch. of Mech. Eng., Tel-Aviv Univ., Tel-Aviv, Israel (Moshaiov, A.; Abramovich, O.)
Abstract	In the last decade evolutionary multi-objective optimizers have been employed in studies concerning evolutionary robotics. In particular, the majority of such studies involve the evolution of neuro-controllers using either a genetic algorithm approach or an evolution strategies approach. Given the fundamental difference between these types of search mechanisms, a valid question is which kind of multi-objective optimizer is better for such applications. This question, which is dealt with here, is raised in view of the permutation problem that exists in evolutionary neural-networks. Two well-known Multi-objective Evolutionary Algorithms are used in the current comparison, namely MO-CMA-ES and NSGA-II. A multi-objective navigation problem is used for the testing, which is known to suffer from a local Pareto problem. For the employed simulation case MO-CMA-ES is better at finding a large sub-set of the approximated Pareto-optimal neuro-controllers, whereas NSGA-II is better at finding a complementary sub-set of the optimal controllers. This suggests that, if this phenomenon persists over a large range of case studies, then future studies should consider some modifications to such algorithms for the multi-objective evolution of neuro-controllers.
Starting Page	2809
Ending Page	2816
File Size	1670096
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Erbium Robot sensing systems Mobile robots Sensor phenomena and characterization Approximation algorithms
Content Type	Text
Resource Type	Article

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