NDLI: Randomized and Rank Based Differential Evolution

Content Provider	IEEE Xplore Digital Library
Author	Urfalioglu, O. Arikan, O.
Copyright Year	2009
Abstract	Many real world problems which can be assigned to the machine learning domain are inverse problems. The available data is often noisy and may contain outliers, which requires the application of global optimization. Evolutionary Algorithms (EA's) are one class of possible global optimization methods for solving such problems. Within population based EA's, Differential Evolution (DE) is a widely used and successful algorithm. However, due to its differential update nature, given a current population, the set of possible new populations is finite and a true subset of the cost function domain. Furthermore, the update formula of DE does not use any information about the fitnesses of the population. This paper presents a novel extension of DE called Randomized and Rank based Differential Evolution (R2DE) to improve robustness and global convergence speed on multimodal problems by introducing two multiplicative terms in the DE update formula. The first term is based on a random variate of a Cauchy distribution, which leads to a randomization. The second term is based on ranking of individuals, so that R2DE exploits additional information provided by the fitnesses. In experiments including non-linear dimension reduction by autoencoders, it is shown that R2DE improves robustness and speed of global convergence.
Starting Page	95
Ending Page	100
File Size	354774
Page Count	6
File Format	PDF
ISBN	9780769539263
DOI	10.1109/ICMLA.2009.29
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic mutations Optimization methods Evolutionary computation Proposals Cauchy distribution Evolutionary optimization Convergence Inverse problems Ranking Differential Evolution Machine learning Automatic control Cost function Robustness
Content Type	Text
Resource Type	Article

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