A Multicriteria Statistical Based Comparison Methodology for Evaluating Evolutionary Algorithms

Carrano, Eduardo G.; Wanner, Elizabeth F.; Takahashi, Ricardo H. C.

doi:10.1109/tevc.2010.2069567

Cited by 40 publications

(28 citation statements)

References 24 publications

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“…There can be different ways (e.g. see the recent work of Carrano et al ), and the description of a simple but practical technique, which was used, for example, by Fraser and Arcuri , is here provided.…”

Section: Multiple Testsmentioning

confidence: 99%

A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering

Arcuri

Briand

2012

Softw. Test. Verif. Reliab.

513

350

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Section: Multiple Testsmentioning

confidence: 99%

A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering

Arcuri

Briand

2012

Softw. Test. Verif. Reliab.

513

350

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“…Carrano [3] showed that evolutionary algorithms cannot be compared only by means of computational performance. Being stochastic search heuristics, it is feasible that each execution have a different result.…”

Section: B Comparative Analysis Of De Algorithmsmentioning

confidence: 99%

“…To differentiate solutions obtained in an MO analysis, an approach quite widespread in the literature is the concept of Pareto Dominance. According to [3], the concept of Pareto Dominance can be used to compare feasible solutions to a problem. Given two solutions, x and y, it is said that x dominates y (denoted x y) if the following conditions are met:…”

Section: B Comparative Analysis Of De Algorithmsmentioning

confidence: 99%

A novel mathematical modeling approach to the electric dispatch problem: Case study using Differential Evolution algorithms

Marcelino

Wanner

Almeida

2013

2013 IEEE Congress on Evolutionary Computation

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Nowadays, the population growth and economic development causes the need for electricity power to increase every year. An unit dispatch problem is defined as the attribution of operational values to each generation unit inside a power plant, given some criteria to be obeyed like the total power to be generated, operational bounds of these units etc. In this context, an optimal dispatch programming for hydroelectric units in energy plants provides a bigger production of electricity to be generated with a minimal water amount. This paper presents an optimization solution for hydroelectric generating system of a plant, using Differential Evolution algorithms. The novel mathematical model proposed and validation of the obtained algorithms will be performed with practical simulation experiments. Throughout the text, the equations and models for the system simulation will be fully described, and the experiments and results will be objectively analysed through statistical inference.Simulation results indicate savings of 6.5 million litres of water for each month of operation using the proposed solution.

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“…In [12], Carrano et al proposed a comparison methodology for evaluation of the performance of evolutionary algorithms. This comparison methodology is based on a multicriteria analysis where each criterion is analyzed by constructing a ranking of the algorithms under analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The criteria used for evaluate their methodology involved a trade-off between the computational cost of the comparing algorithms and the quality of the solution achieved by each of them. This algorithm evaluation (an others discussed in [12]) tries to deal with both learning tasks: that of fitting parameters to some training data and, then, selecting the best model. In our particular case though, we pursue a different objective since we want a method able to evaluate predictive models already deployed into a DSS, which gives us a measure of how well these classification models are working in the system according to the data introduced along time by the users for diagnosis support.…”

Section: Introductionmentioning

confidence: 99%

An audit method suited for decision support systems for clinical environment

Vicente

Tortajada

Fuster–Garcia

et al. 2012

2012 6th IEEE INTERNATIONAL CONFERENCE INTELLIGENT SYSTEMS

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We present a novel on-line method to audit predictive models using a Bayesian perspective. The auditing model has been specifically designed for decision support systems (DSSs) suited for clinical environments. Taking as starting point the proposed diagnosis supplied by the clinician, our method compares and evaluates the predictive skills of those models able to answer to that diagnosis. Our approach consists in calculating the posterior odds of a model through the composition of three different odds: prior, static and dynamic. To do so, our method estimates the posterior odds from the cases that the comparing models had in common during the design stage and from the cases already viewed by the DSS after deployment in the clinical site. In addition, if an ontology of the classes is available, our method can audit models answering related questions, which offers a reinforcement to the decisions the user already took and gives orientation on further diagnostic steps. The main technical novelty of this approach lies in the design of an audit model adapted to suit the decision workflow of a clinical environment. The audit model allows deciding which is the classifier that best suits each particular case under evaluation and allows the detection of possible misbehaviours due to population differences or systematic errors in the clinical site. We show the efficacy of our method for the problem of brain tumour diagnosis with magnetic resonance spectroscopy.

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A Multicriteria Statistical Based Comparison Methodology for Evaluating Evolutionary Algorithms

Cited by 40 publications

References 24 publications

A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering

A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering

A novel mathematical modeling approach to the electric dispatch problem: Case study using Differential Evolution algorithms

An audit method suited for decision support systems for clinical environment

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