Elena Briones Pérez

Finding multiple solutions in job shop scheduling by niching genetic algorithms

The interest in multimodal optimization methods is increasing in the last years. The objective is to find multiple solutions that allow the expert to choose the solution that better adapts to the actual conditions. Niching methods extend genetic algorithms to domains that require the identification of multiple solutions. There are different niching genetic algorithms: sharing, clearing, crowding and sequential, etc. The aim of this study is to study the applicability and the behavior of several niching genetic algorithms in solving job shop scheduling problems, by establishing a criterion in the use of different methods according to the needs of the expert. We will experiment with different instances of this problem, analyzing the behavior of the algorithms from the efficacy and diversity points of view.

Multiple Crossover per Couple with Selection of the Two Best Offspring: An Experimental Study with the BLX-alpha Crossover Operator for Real-Coded Genetic Algorithms

In this paper, we propose a technique for the application of the crossover operator that generates multiple descendants from two parents and selects the two best offspring to replace the parents in the new population. In order to study the proposal, we present different instances based on the BLX-? crossover operator for real-coded genetic algorithms. In particular, we investigate the influence of the number of generated descendants in this operator, the number of evaluations, and the value for the parameter ?. Analyzing the experimentation that we have carried out, we can observe that it is possible, with multiple descendants, to achieve a suitable balance between the explorative properties associated with BLX-? and the high selective pressure associated to the selection of the two best descendants.

Taking advantage of solving the resource constrained multi-project scheduling problems using multi-modal genetic algorithms

In this paper, for the first time, multi-modal genetic algorithms (MMGAs) are proposed to optimize the resource constrained multi-project scheduling problem (RCMPSP). In problems where the landscape has both multiple local and global optima, such as the RCMPSP, a MMGAs approach can provide managers with an advantage in decision-making because they can choose between alternative solutions equally good. Alternative optima are achieved because the diversification techniques of MMGAs introduce diversity in population, decreasing the possibility of the optimization process getting caught in a unique local or global optimum. To compare the performance of a MMGAs approach with other alternative approaches, commonly accepted by researchers to solve the RCMPSP such as classical genetic algorithms and dispatching heuristics based on priority rules, we analyse two time-based objective functions (makespan and average percent delay) and three coding systems [random keys (RK), activity list (AL), and a new proposal called priority rule (PR)]. We have found that MMGAs significantly improve the efficacy (the algorithm’s capability to find the best optimum) and the multi-solution-based efficacy (the algorithm’s capability to find multiple optima) of the other two approaches. For makespan the PR is the best code in terms of the efficacy and multi-solution-based efficacy, and the RK is the best code for the average percent delay.

A Hardware/Software Partitioning and Scheduling Approach for Embedded Systems with Low-Power and High Performance Requirements

Hardware/software (hw/sw) partitioning largely affects the system cost, performance, and power consumption. Most of the previous hw/sw partitioning approaches are focused on either optimising the hw area, or the performance. Thus, they ignore the influence of the partitioning process on the energy consumption. However, during this process the designer still has the maximum flexibility, hence, it is clearly the best moment to analyse the energy consumption. We have developed a new hw/sw partitioning and scheduling tool that reduces the energy consumption of an embedded system while meeting high performance constraints. We have applied it to two current multimedia applications saving up to 30% of the system energy without reducing the performance.

La co-docencia en el contexto universitario como estrategia para la innovación docente

La co-docencia ha sido descrita como una de las actuaciones educativas de exito en el contexto universitario. Supone practicas de ensenanza cooperativa desarrollada por dos o mas docentes generando un marco de retroalimentacion constante para el profesorado implicado, al compartir su experiencia y conocimientos durante el desarrollo de las clases. En este trabajo se presenta el diseno y valoracion de una experiencia de co-docencia desarrollada en la Facultad de Educacion de la Universidad de Cantabria. Esta experiencia educativa se disena como una modalidad de co-docencia en estaciones a traves de la mentoria entre estudiantes. Se abordan desde la perspectiva de las docentes implicadas en el desarrollo de la co-docencia, sus expectativas iniciales, los beneficios que segun ellas se deriva de esta practica educativa, las dificultaddes encontradas y las propuestas de mejora para la continuidad de esta practica docente. Asimismo se recogen algunos resultados de aprendizaje en los estudiantes. Los resultados del trabajo ponen de manifiesto la valoracion positiva de la experiencia y el compromiso de las docentes con este tipo de actividades a pesar de las limitaciones temporales y organizativas encontradas. Palabras clave: co-docencia, innovacion educativa, Educacion Superior, metodologia, mentoria.