Comparison between Single-Objective and Multi-Objective Genetic Algorithms: Performance Comparison and Performance Measures

Ishibuchi, Hisao; Nojima, Yusuke; Doi, Tomoharu

doi:10.1109/cec.2006.1688438

Cited by 50 publications

(24 citation statements)

References 13 publications

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“…The works in [13] shows that common MOEA measures such as hypervolume [31] are not necessarily suitable for comparing solutions by MOEAs (our BLiM approach) with solutions by SOEAs (baseline in this work). Therefore, in order to compare the baseline approach with BLiM, we first take the best solution of the baseline approach for its single-objective (the similarity with the bug description), and then we take the best solution of BLiM with regard to the objective of the baseline approach (the similarity with the bug description), as described in [13]. Finally, these solutions are compared to the bug realization of the oracle in order to get a confusion matrix.…”

Section: Methodsmentioning

confidence: 96%

On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

Arcega

Font

Haugen

et al. 2018

Lecture Notes in Information Systems and Organisation

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Bug location is a common task in Software Engineering, specially when maintaining and evolving software products. When locating bugs in code, results depend greatly on the way code modification timespans are weighted. However, the influence of timespan weightings on bug location in models has not received enough attention yet. Throughout this paper, we analyze the influence of several timespan weightings on bug location in models. These timespan weightings guide an evolutionary algorithm, which returns a ranking of model fragments relevant to the solution of a bug. We evaluated our timespan weightings in BSH, a real-world industrial case study, by measuring the results in terms of recall, precision, and F-measure. Results show that the use of the most recent timespan model modifications provide the best results in our study. We also performed a statistical analysis to provide evidence of the significance of the results.

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Section: Methodsmentioning

confidence: 96%

On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

Arcega

Font

Haugen

et al. 2018

Lecture Notes in Information Systems and Organisation

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“…We denote this test problem as the 2-500 problem. The two objectives f 1 (x) and f 2 (x) of the 2-500 problem were generated by randomly assigning an integer in the closed interval [10,100] to each item as its profit (see [34]). In the same manner, we generated other two objectives f 3 (x) and f 4 (x).…”

Section: Computational Experiments On Knapsack Problemsmentioning

confidence: 99%

“…Pareto dominance is used for fitness evaluation in almost all well-known and frequently-used EMO algorithms such as NSGA-II [4], SPEA [34] and SPEA2 [33]. Whereas Pareto dominance-based EMO algorithms usually work very well on multi-objective problems with two or three objectives, they often show difficulties in the handling of many-objective problems with four or more objectives as pointed out in several studies [7], [10], [16], [23], [24], [35]. This is because almost all individuals in the current population are non-dominated with each other when they are compared using many objectives.…”

Section: Introductionmentioning

confidence: 99%

Effects of the Existence of Highly Correlated Objectives on the Behavior of MOEA/D

Ishibuchi

Hitotsuyanagi

Ohyanagi

et al. 2011

Lecture Notes in Computer Science

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Abstract.Recently MOEA/D (multi-objective evolutionary algorithm based on decomposition) was proposed as a high-performance EMO (evolutionary multiobjective optimization) algorithm. MOEA/D has high search ability as well as high computational efficiency. Whereas other EMO algorithms usually do not work well on many-objective problems with four or more objectives, MOEA/D can properly handle them. This is because its scalarizing function-based fitness evaluation scheme can generate an appropriate selection pressure toward the Pareto front without severely increasing the computation load. MOEA/D can also search for well-distributed solutions along the Pareto front using a number of weight vectors with different directions in scalarizing functions. Currently MOEA/D seems to be one of the best choices for multi-objective optimization in various application fields. In this paper, we examine its performance on multi-objective problems with highly correlated objectives. Similar objectives to existing ones are added to two-objective test problems in computational experiments. Experimental results on multi-objective knapsack problems show that the inclusion of similar objectives severely degrades the performance of MOEA/D while it has almost no negative effects on NSGA-II and SPEA2. We also visually examine such an undesirable behavior of MOEA/D using manyobjective test problems with two decision variables.

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“…Although an approximation of the Pareto front may, in principle, be traced out for a convex multiobjective model by varying the weights assigned to criteria in a single objective function, the accuracy of this approximation depends sensitively on the method used to assign weights to the criteria [54]. The most popular method of weight assignment is the linear weighted sum method, but many other schemes also exist [30,31,32,33,35,48,55]. Attempts at finding weighting methods that yield close Pareto front approximations may be tedious and are usually highly problem-specific.…”

Section: Literature Reviewmentioning

confidence: 99%

A decision support system for firebase location in a nature conservation area

Reed

Vuuren

2017

ORiON

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It is important that firebases are available on standby at strategic locations in a nature conservation area from where wildfire ignition points can be reached rapidly and such fires brought under control before they spread. Two facility location models are proposed in this paper which may form the basis for decision support when deciding on the locations of such firebases in a nature conservation area. Both of these models are multi-objective in nature. They are able to produce solutions that embody trade-off decisions between minimising the cost of locating firebases and maximising the coverage of key areas in a conservation area. These trade-offs may be based on a variety of coverage importance criteria, such as aiming to cover terrain portions exhibiting a steep ground slope, terrain portions that experience a high annual mean wind speed, or terrain portions in which many wildfires have ignited in the past. The coverage criteria are typically case-specific and may therefore be specified by the decision maker. Both models, as well as their approximate solution methodology, are implemented in the form of a computerised decision support system in order to render them accessible to non-mathematically inclined decision makers. The decision support system is validated by applying it to a special case study involving Table Mountain National Park, a nature conservation area in the Western Cape, South Africa.

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Comparison between Single-Objective and Multi-Objective Genetic Algorithms: Performance Comparison and Performance Measures

Cited by 50 publications

References 13 publications

On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

Effects of the Existence of Highly Correlated Objectives on the Behavior of MOEA/D

A decision support system for firebase location in a nature conservation area

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