A Random Forest Assisted Evolutionary Algorithm for Data-Driven Constrained Multi-Objective Combinatorial Optimization of Trauma Systems for publication

Many real-world optimization problems can be solved by using the data-driven approach only, simply because no analytic objective functions are available for evaluating candidate solutions. In this work, we address a class of expensive datadriven constrained multi-objective combinatorial optimization problems, where the objectives and constraints can be calculated only on the basis of large amount of data. To solve this class of problems, we propose to use random forests and radial basis function networks as surrogates to approximate both objective and constraint functions. In addition, logistic regression models are introduced to rectify the surrogate-assisted fitness evaluations and a stochastic ranking selection is adopted to further reduce the influences of the approximated constraint functions. Three variants of the proposed algorithm are empirically evaluated on multi-objective knapsack benchmark problems and two realworld trauma system design problems. Experimental results demonstrate that the variant using random forest models as the surrogates are effective and efficient in solving data-driven constrained multi-objective combinatorial optimization problems

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

This paper addresses the multiobjective hybrid flow shop (MOHFS) scheduling problem. In the MOHFS problem considered here, we have a set of jobs that must be performed in a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. The evaluation criteria are the minimizations of makespan, the weighted sum of the tardiness, and the weighted sum of the earliness. For solving it, an algorithm based on the multiobjective general variable neighborhood search (MO-GVNS) metaheuristic, named adapted MO-GVNS, is proposed. This work also presents and compares the results obtained by the adapted MO-GVNS with those of four other algorithms: multiobjective reduced variable neighborhood search, nondominated sorting genetic algorithm II (NSGA-II), and NSGA-III, and another MO-GVNS from the literature. The results were evaluated based on the Hypervolume, Epsilon, and Spacing metrics, and statistically validated by the Levene test and confidence interval charts. The results showed the efficiency of the proposed algorithm for solving the MOHFS problem

An MO-GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem.

This paper addresses the multiobjective hybrid flow shop (MOHFS) scheduling problem. In the MOHFS problem considered here, we have a set of jobs that must be performed in a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. The evaluation criteria are the minimizations of makespan, the weighted sum of the tardiness, and the weighted sum of the earliness. For solving it, an algorithm based on the multiobjective general variable neighborhood search (MO-GVNS) metaheuristic, named adapted MO-GVNS, is proposed. This work also presents and compares the results obtained by the adapted MO-GVNS with those of four other algorithms: multiobjective reduced variable neighborhood search, nondominated sorting genetic algorithm II (NSGA-II), and NSGA-III, and another MO-GVNS from the literature. The results were evaluated based on the Hypervolume, Epsilon, and Spacing metrics, and statistically validated by the Levene test and confidence interval charts. The results showed the efficiency of the proposed algorithm for solving the MOHFS problem

Anales del XIII Congreso Argentino de Ciencias de la Computación (CACIC)

Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterogéneas Redes de Avanzada Redes inalámbricas Redes móviles Redes activas Administración y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad informática y autenticación, privacidad Infraestructura para firma digital y certificados digitales Análisis y detección de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integración (Web Services o .Net)Red de Universidades con Carreras en Informática (RedUNCI

Anales del XIII Congreso Argentino de Ciencias de la Computación (CACIC)

Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterogéneas Redes de Avanzada Redes inalámbricas Redes móviles Redes activas Administración y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad informática y autenticación, privacidad Infraestructura para firma digital y certificados digitales Análisis y detección de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integración (Web Services o .Net)Red de Universidades con Carreras en Informática (RedUNCI