Indicator-Based Selection in Multiobjective Search

Zitzler, Eckart; Künzli, Simon

doi:10.1007/978-3-540-30217-9_84

Cited by 1,567 publications

(1,051 citation statements)

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“…An evaluation of the aggregated values reached by the individuals in the final population is necessary to identify target vectors that support these solutions and accordingly refine the set of target vectors 4 . The term indicator-based EA (IBEA) was introduced by Zitzler and Künzli [20] for EMOA guided by a general preference information. The EMOA's selection operator uses a preference function (indicator) as a single-objective substitute for the d-dimensional objective function.…”

Section: Resultsmentioning

confidence: 99%

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Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization

Wagner¹,

Beume

Naujoks

Lecture Notes in Computer Science

348

227

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Abstract. Research within the area of Evolutionary Multi-objective Optimization (EMO) focused on two-and three-dimensional objective functions, so far. Most algorithms have been developed for and tested on this limited application area. To broaden the insight in the behavior of EMO algorithms (EMOA) in higher dimensional objective spaces, a comprehensive benchmarking is presented, featuring several state-ofthe-art EMOA, as well as an aggregative approach and a restart strategy on established scalable test problems with three to six objectives. It is demonstrated why the performance of well-established EMOA (NSGA-II, SPEA2) rapidly degradates with increasing dimension. Newer EMOA like ε-MOEA, MSOPS, IBEA and SMS-EMOA cope very well with highdimensional objective spaces. Their specific advantages and drawbacks are illustrated, thus giving valuable hints for practitioners which EMOA to choose depending on the optimization scenario. Additionally, a new method for the generation of weight vectors usable in aggregation methods is presented.

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Section: Resultsmentioning

confidence: 99%

“…A positive scaling constant is invoked, which is chosen as κ = 0.05 as recommended in [20] for the applied adaptive variant of IBEA, which works on normalized indicator values. For dominance preserving indicators holds that the fitness value of a vector is worse than the fitness value of a vector that dominates it.…”

Section: Ibeamentioning

confidence: 99%

Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization

Wagner¹,

Beume

Naujoks

Lecture Notes in Computer Science

348

227

View full text Add to dashboard Cite

show abstract

“…However, finding suitable weights is an optimisation problem in itself. On the other hand, the indicator-based MOEAs typically adopt selection mechanism with specific performance metric (Zitzler and Künzli, 2004;Beume et al, 2007). They have the advantage of being scalable to the number of objectives, usually four or more.…”

Section: 1mentioning

confidence: 99%

A multi-objective evolutionary algorithm-based soft computing model for educational data mining

Tan

Lim

Bong

et al. 2017

AAOUJ

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For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students' online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach -A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide.Findings -The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value -A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

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“…Zitzler and Künzli 81 proposed another method called B−IBEA. The main idea in this method is defining the optimization goal in terms of a binary quality measure and directly using it in the selection process.…”

Section: Computational Evaluationmentioning

confidence: 99%