Exploitation of a Medium-Sized Fuzzy Outranking Relation Based on Multi-objective Evolutionary Algorithms to Derive a Ranking

López, Juan Carlos; Noriega, Jesús Jaime Solano; Alcaráz, Jorge Luis García; Chavira, Diego Alonso Gastélum

doi:10.1080/18756891.2016.1204122

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…Therefore, we need specific procedures in order to derive a consensus ranking. We propose the procedure which finds its roots in [31,58,59]. Our approach for exploitation a fuzzy outranking relation to derive a ranking is to use a multiobjective evolutionary algorithm-based heuristic method.…”

Section: Outranking Approach For Multi-network Disease Gene Prioritizationmentioning

confidence: 99%

An Outranking Approach for Gene Prioritization Using Multinetworks

Noriega¹,

López²,

Browne³

et al. 2021

IJCIS

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High-throughput experimental techniques such as genome-wide association studies have been instrumental in the identification of disease-associated genes. These methods often produce large lists of disease candidate genes which are time-consuming and expensive to experimentally validate. Computational gene prioritization methods are required to identify relevant genes from a larger pool of candidates. Research has shown that the integration of diverse "omic" evidence can reduce the candidategene search space. In this paper we present a general framework that integrates "omic" data using a multinetwork approach and topological analysis to prioritize disease-candidate genes. Specifically, we propose a data integration method within a multicriteria decision analysis context using aggregation mechanisms based on decision rules identifying positive and negative criteria for judging gene-candidates ranks. The proposed multinetwork disease gene prioritization method is applied to the prioritization of disease genes in ovarian cancer progression. Using this approach we uncovered known ovarian cancer genes GSTA1, ERBB2, IL1A, MAGEB2, along with significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways ErbB signaling and pathways in cancer. Relatively high predictive performance (area under Receiver Operating Characteristic [ROC] curve 0.704) was observed when classifying epithelial ovarian high-grade serous carcinoma cancer early and late stage RNA-Seq expression profiles from individuals using 10-fold cross-validation.

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Section: Outranking Approach For Multi-network Disease Gene Prioritizationmentioning

confidence: 99%

An Outranking Approach for Gene Prioritization Using Multinetworks

Noriega¹,

López²,

Browne³

et al. 2021

IJCIS

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“…Guo and Wang 30 proposed a new fuzzy multiobjective lattice order decision method for preference ranking in conflict analysis. In RP 2 -NSGA-II 31 addressed the problem of multi-criteria ranking with a medium-sized set of alternatives as a multiobjective combinatorial optimization problem.…”

Section: Preference-based Moea Approachesmentioning

confidence: 99%

A gene expression programming algorithm for discovering classification rules in the multi-objective space

Guerrero-Enamorado¹,

Morell²,

Ventura³

2018

IJCIS

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Multi-objective evolutionary algorithms have been criticized when they are applied to classification rule mining, and, more specifically, in the optimization of more than two objectives due to their computational complexity. It is known that a multi-objective space is much richer to be explored than a single-objective space. In consequence, there are only few multi-objective algorithms for classification and their empirical assessed is quite limited. On the other hand, gene expression programming has emerged as an alternative to carry out the evolutionary process at genotypic level in a really efficient way. This paper introduces a new multi-objective algorithm for discovering classification rules, AR-NSGEP (Adaptive Reference point based Non-dominated Sorting with Gene Expression Programming). It is a multi-objective evolution of a previous single-objective algorithm. In AR-NSGEP, the multi-objective search was based on the wellknown R-NSGA-II algorithm, replacing GA with GEP technology. Four objectives led the rules-discovery process, three of them (sensitivity, specificity and precision) were focused on promoting accuracy and the fourth (simpleness) on the interpretability of rules. AR-NSGEP was evaluated on several benchmark data sets and compared against six rule-based classifiers widely used. The AR-NSGEP, with four-objectives, achieved a significant improvement of the AUC metric with espect to most of the algorithms assessed, while the predictive accuracy and number of rules in the obtained models reached to acceptable results.

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“…The multiobjective optimization problem (MOP, the abbreviations and their meanings are given in Table 1) is an optimization problem that may have a number of conflicting objectives to be considered, and decision-makers need to determine an optimal trade-off among the objectives. This problem presents in many real-life applications [1][2][3][4]. A Pareto optimal solution to an MOP is a candidate for the optimal trade-off [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems

et al. 2019

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This paper considers the multiobjective scheduling of flexible manufacturing systems (FMSs). Due to high degrees of route flexibility and resource sharing, deadlocks often exhibit in FMSs. Manufacturing tasks cannot be finished if any deadlock appears. For solving such problem, this work develops a deadlock-free multiobjective evolutionary algorithm based on decomposition (DMOEA/D). It intends to minimize three objective functions, i.e., makespan, mean flow time, and mean tardiness time. The proposed algorithm can decompose a multiobjective scheduling problem into a certain number of scalar subproblems and solves all the subproblems in a single run. A type of a discrete differential evolution (DDE) algorithm is also developed for solving each subproblem. The mutation operator of the proposed DDE is based on the hamming distance of two randomly selected solutions, while the crossover operator is based on Generalization of Order Crossover. Experimental results demonstrate that the proposed DMOEA/D can significantly outperform a Pareto domination-based algorithm DNSGA-II for both 2-objective and 3-objective problems on the studied FMSs.

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Exploitation of a Medium-Sized Fuzzy Outranking Relation Based on Multi-objective Evolutionary Algorithms to Derive a Ranking

Cited by 9 publications

References 29 publications

An Outranking Approach for Gene Prioritization Using Multinetworks

An Outranking Approach for Gene Prioritization Using Multinetworks

A gene expression programming algorithm for discovering classification rules in the multi-objective space

A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems

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