A phenotypic analysis of three population-based metaheuristics

Metaheuristics are used as very good optimization methods and they imitate natural, biologic, social and cultural process. In this work, we evaluate and compare three different metaheuristics which are population-based: Genetic Algorithms, CHC and Scatter Search. They work with a set of solutions in contrast to trajectory-based metaheuristics which use an only solution. From a comparative analysis, we can infer that Genetic Algorithms and CHC algorithms can solve satisfactorily problems with a growing complexity. While Scatter Search provides high quality solutions but its computational effort is very high too.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

A modified binary-PSO for continuous optimization

Metaheuristics based on swarm intelligence simulate the behavior of a biological social system like as a flock of birds or a swarm of bees, and they have achieved important advances for solving optimization problems. In this paper, we propose a variant for a particular kind of those metaheurisitcs: Particle Swarm Optimization (PSO). This modification arises after discovering a low rate of convergence produced by a high level of dispersal at the swarm. Finally, we analyzed and compared the results obtained by an original PSO algorithm and our proposal. From those, we can see the improvement obtained by our variant since it allows to explore more the search space.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

A phenotypic analysis of three population-based metaheuristics

Metaheuristics are used as very good optimization methods and they imitate natural, biologic, social and cultural process. In this work, we evaluate and compare three different metaheuristics which are population-based: Genetic Algorithms, CHC and Scatter Search. They work with a set of solutions in contrast to trajectory-based metaheuristics which use an only solution. From a comparative analysis, we can infer that Genetic Algorithms and CHC algorithms can solve satisfactorily problems with a growing complexity. While Scatter Search provides high quality solutions but its computational effort is very high too.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

A modified binary-PSO for continuous optimization

Metaheuristics based on swarm intelligence simulate the behavior of a biological social system like as a flock of birds or a swarm of bees, and they have achieved important advances for solving optimization problems. In this paper, we propose a variant for a particular kind of those metaheurisitcs: Particle Swarm Optimization (PSO). This modification arises after discovering a low rate of convergence produced by a high level of dispersal at the swarm. Finally, we analyzed and compared the results obtained by an original PSO algorithm and our proposal. From those, we can see the improvement obtained by our variant since it allows to explore more the search space.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Solving the two dimensional cutting problem using evolutionary algorithms with penalty functions

In this work a solution using evolutionary algorithms with penalty function for the non-guillotine cutting problem is presented. In this particular problem, the rectangular pieces have to be cut from an unique large object, being the goal to maximize the total value of cut pieces. Some chromosomes can hold pieces to be cut, but some pieces cannot be arranged into the object, generating infeasible solutions. A way to deal with this kind of solutions is to use a penalizing strategy. The used penalty functions have been originally developed for the knapsack problem and they are adapted for the cutting problem in this paper. Moreover, the effect on the algorithm performance to combine penalty functions with two different selection methods (binary tournament and roulette wheel) is studied. The algorithm uses a binary representation, one-point crossover, big-creep mutation and in order to evaluated the quality of solutions a placement routine is considered (Heuristic with Efficient Management of Holes). Experimental comparisons of the performance of the resulting algorithms are carried out using publicly available benchmarks to the non-guillotine cutting problem. We report on the high performance of the proposed models at similar (or better) accuracy with respect to existing algorithms.VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Solving the two dimensional cutting problem using evolutionary algorithms with penalty functions

In this work a solution using evolutionary algorithms with penalty function for the non-guillotine cutting problem is presented. In this particular problem, the rectangular pieces have to be cut from an unique large object, being the goal to maximize the total value of cut pieces. Some chromosomes can hold pieces to be cut, but some pieces cannot be arranged into the object, generating infeasible solutions. A way to deal with this kind of solutions is to use a penalizing strategy. The used penalty functions have been originally developed for the knapsack problem and they are adapted for the cutting problem in this paper. Moreover, the effect on the algorithm performance to combine penalty functions with two different selection methods (binary tournament and roulette wheel) is studied. The algorithm uses a binary representation, one-point crossover, big-creep mutation and in order to evaluated the quality of solutions a placement routine is considered (Heuristic with Efficient Management of Holes). Experimental comparisons of the performance of the resulting algorithms are carried out using publicly available benchmarks to the non-guillotine cutting problem. We report on the high performance of the proposed models at similar (or better) accuracy with respect to existing algorithms.VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Algoritmos metaheurísticos para optimización y aplicación a problemas NP completos

En la actualidad las empresas deben enfrentar un conjunto de problemas logístico-operativos, de alta complejidad, conocidos en la comunidad científica como problemas de optimización combinatoria. Actualmente, en esta comunidad se observa una importante tendencia a resolver dichos problemas con la utilización de algoritmos heurísticos y metaheurísticos. Nuestro grupo está abocado al diseño y desarrollo de algoritmos heurísticos y metaheurísticos que resuelvan problemas de optimización. En particular se ha puesto especial énfasis en: el problema de corte y empaquetado, y en el de planificación y programación de recursos y en el ensamblado de fragmentos de ADN. Tanto la optimización de la planificación de recursos como la de generación de patrones de cortes, reducen significativamente los costos de los distintos recursos involucrados. Esto se debe a la mejor utilización que se hace de los mismos, lograda por medio de la aplicación de metaheurísticas. Por otro lado, las metaheurísticas también permiten resolver problemas de optimización en el área de la bioinformática; la cual se beneficia con la capacidad de hacer búsquedas en el espacio de problemas realmente grandes, en un tiempo razonable sin necesidad del uso de información extra. Estas son ventajas que no ofrecen los algoritmos específicos de esta área. Ya sea en el contexto industrial como en el bioinformático, las metaheurísticas han sido juzgadas o evaluadas como beneficiosas, ya que con un esfuerzo limitado se pueden alcanzar buenos resultados con gran versatilidad. Actualmente dos de las ramas con más éxito para diseñar metaheurísticas eficientes, y dar solución a estos problemas, son la hibridación y el paralelismo.Eje: Agentes y Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Metaheurísticas aplicadas a problemas de optimización

El objetivo principal de esta línea de investigación es el diseño y desarrollo de algoritmos heurísticos y meta-heurísticos que resuelvan problemas de optimización. En particular se abordan problemas de corte y empaquetado, de ruteo de vehículos y de ensamblado de fragmentos de ADN. Actualmente dos de las ramas con más éxito para diseñar metaheurísticas eficientes, y dar solución a estos problemas, son la hibridación y el paralelismo. El trabajo está orientado a aplicar metaheurísticas secuenciales y paralelas a los problemas propuestos, a analizar los resultados para comprender el comportamiento de estos algoritmos y a proponer nuevos métodos para resolver los problemas de una manera más eficaz y eficiente.Eje: Agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Metaheurísticas aplicadas a problemas de optimización

El objetivo principal de esta línea de investigación es el diseño y desarrollo de algoritmos heurísticos y meta-heurísticos que resuelvan problemas de optimización. En particular se abordan problemas de corte y empaquetado, de ruteo de vehículos y de ensamblado de fragmentos de ADN. Actualmente dos de las ramas con más éxito para diseñar metaheurísticas eficientes, y dar solución a estos problemas, son la hibridación y el paralelismo. El trabajo está orientado a aplicar metaheurísticas secuenciales y paralelas a los problemas propuestos, a analizar los resultados para comprender el comportamiento de estos algoritmos y a proponer nuevos métodos para resolver los problemas de una manera más eficaz y eficiente.Eje: Agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele