Resumo Este artigo apresenta os fatores preditivos à evasão segundo os estudantes dos cursos Ciência da Comunicação, Design de Comunicação, Desporto, Educação Social, Educação Básica, Imagem Animada da Escola Superior de Educação e Comunicação (ESEC) da Universidade do Algarve/Portugal. O instrumento aplicado, entre setembro e dezembro de 2015, foi a "Escala de Motivos para Evasão do Ensino Superior − M-ES" cujo objetivo é avaliar os motivos potenciais que levam os estudantes a deixar o ensino superior. Os dados recolhidos foram transcritos para o programa SPSS (Statistical Package for the social Sciences) que mediu o impacto dos motivos listados na M-ES em 68 itens, com chave de resposta em Escala Likert de 5 pontos. Este estudo concluiu que a M-ES pode contribuir para o aprofundamento do conhecimento a respeito da evasão no ensino superior e amparar programas institucionais para redução da evasão. Palavras-chave: Educação superior. Fatores preditivos. Evasão. Motives for evading the School of Education and Communication of the University of Algarve/Portugal, according to students AbstractThis article presents the predictive factors to avoidance according to the students of the courses of Communication Science, Communication Design, Sports, Social Education, Basic Education, Animated Image of the School of Education and Communication (ESEC) of the University of Algarve/PT. The instrument applied between September and December 2015 was the "Scale of Motives for Evasion of Higher Education − M-ES", whose objective is to evaluate the potential reasons that lead students to leave higher education. The collected data were transcribed into the SPSS (Statistical Package for the Social Sciences) program which measured the impact of the reasons listed in the M-ES in 68 items, with a 5-point Likert Scale response key. This study concluded that M-ES can contribute to the deepening of knowledge about avoidance in higher education and to support institutional programs to reduce evasion.

This paper presents an architecture which is suitable for a massive parallelization of the compact genetic algorithm. The resulting scheme has three major advantages. First, it has low synchronization costs. Second, it is fault tolerant, and third, it is scalable.The paper argues that the benefits that can be obtained with the proposed approach is potentially higher than those obtained with traditional parallel genetic algorithms.

Abstract. This paper presents an architecture which is suitable for a massive parallelization of the compact genetic algorithm. The resulting scheme has three major advantages. First, it has low synchronization costs. Second, it is fault tolerant, and third, it is scalable. The paper argues that the benefits that can be obtained with the proposed approach is potentially higher than those obtained with traditional parallel genetic algorithms. In addition, the ideas suggested in the paper may also be relevant towards parallelizing more complex probabilistic model building genetic algorithms.