Abstract-the use of Linux-based clusters is a strategy for the development of multiprocessor systems. These types of systems face the problem of efficiently executing the planning and allocation of tasks, for the efficient use of its resources. This paper addresses this as a multi-objective problem, carrying out an analysis of the objectives that are opposed during the planning of the tasks, which are waiting in the queue, before assigning tasks to processors. For this, we propose a method that avoids strategies such as those that use genetic operators, exhaustive searches of contiguous free processors on the target system, and the use of the strict allocation policy: First Come First Serve (FIFO). Instead, we use estimation and simulation of the joint probability distribution as a mechanism of evolution, for obtaining assignments of a set of tasks, which are selected from the waiting queue through the planning policy Random-Orderof-Service (ROS). A set of conducted experiments that compare the results of the FIFO allocation policy, with the results of the proposed method show better results in the criteria of: utilization, throughput, mean turnaround time, waiting time and the total execution time, when system loads are significantly increased.

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In this chapter a hybrid algorithm is constructed, implemented and tested for the optimization of graph drawing employing a multiobjective approach. The multiobjective optimization problem for graph drawing consists of three objective functions: minimizing the number of edge crossing, minimizing the graph area, and minimizing the aspect ratio. The population of feasible solutions is generated using a hybrid algorithm and at each step a Pareto front is calculated. This hybrid algorithm combines a global search algorithm (EDA — Estimation of Distribution Algorithm) with a local search Algorithm (HC — Hill Climbing) in order to maintain a balance between the exploration and exploitation. Experiments were performed employing planar and non-planar graphs. A quality index of the obtained solutions by the hybrid MOEA-HCEDA (Multiobjective Evolutionary Algorithm - Hill Climbing & Univariate Marginal Distribution Algorithm) is constructed based on the Pareto front defined in this chapter. A factorial experiment using the algorithm parameters was performed. The factors are number of generations and population size, and the result is the quality index. The best combination of factors levels is obtained.

This chapter presents the implementation of a Genetic Algorithm into a framework for machine learning that deals with the problem of identifying the factors that impact the health state of newborns in Mexico. Experimental results show a percentage of correct clustering for unsupervised learning of 89%, a real life training matrix of 46 variables, was reduced to only 25 that represent 54% of its original size. Moreover execution time is about one and a half minutes. Each risk factor (of neonatal health) found by the algorithm was validated by medical experts. The contribution to the medical field is invaluable, since the cost of monitoring these features is minimal and it can reduce neonatal mortality in our country.