NDLI: Online knowledge-based evolutionary multi-objective optimization

Content Provider	IEEE Xplore Digital Library
Author	Bin Zhang Shafi, K. Abbass, H.
Copyright Year	2014
Description	Author affiliation: Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia (Bin Zhang; Shafi, K.; Abbass, H.)
Abstract	Knowledge extraction from a multi-objective optimization process has important implications including a better understanding of the optimization process and the relationship between decision variables. The extant approaches, in this respect, rely on processing the post-optimization Pareto sets for automatic rule discovery using statistical or machine learning methods. However such approaches fall short of providing any information during the progress of the optimization process, which can be critical for decision analysis especially if the problem is dynamic. In this paper, we present a multi-objective optimization framework that uses a knowledge-based representation to search for patterns of Pareto optimal design variables instead of conventional point form solution search. The framework facilitates the online discovery of knowledge during the optimization process in the form of interpretable rules. The core contributing idea of our research is that we apply multi-objective evolutionary process on a population of bounding hypervolumes, or rules, instead of evolving individual point-based solutions. The framework is generic in a sense that any existing multi-objective optimization algorithm can be adapted to evaluate the rule quality based on the sampled solutions from the bounded space. An instantiation of the framework using hyperrectangular representation and non-dominated sorting based rule evaluation is presented in this paper. Experimental results on a specifically designed test function as well as some standard test functions are presented to demonstrate the working and convergence properties of our algorithm.
Starting Page	2222
Ending Page	2229
File Size	413652
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900610
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	Sociology Algorithm design and analysis Pareto optimization Sorting Evolutionary computation
Content Type	Text
Resource Type	Article

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