{ a e t p i e k a r s k i , m a u r o m , t o n y . h i l d , m h m u l a t i } @ g m a i l . c o m , d k i k u t i @ d i n . u e m . b r Abstract. This paper describes the experience of an extension project for the training in computer programming, using the methodology of programming contest and aiming at the selection of teams to participate in the First Phase of the SBC Programming Contest. In addition to describing the carried out activities and present some project results, this paper aims to share the adopted methodology, in order to discuss possibilities and strategies for future activities.Resumo. Este artigo descreve a experiência de um projeto de extensão destinado ao treinamento em programação de computadores, utilizando a metodologia das maratonas de programação e visando a seleção de times para participação na Primeira Fase da Maratona de Programação da SBC. Além de descrever as atividades realizadas e apresentar alguns resultados do projeto, o artigo visa compartilhar a metodologia adotada, a fim de discutir possibilidades e estratégias para atividades futuras.

This paper proposes the algorithm based on the metaheuristic Ant Colony Optimization (ACO) called Ant-Line, which uses the line-oriented approach for the set covering problem, that is an innovative and emerging approach in the context, beyond the use of a local search. The algorithm is compared with other ACO-based approaches. The results obtained are promising and have reached good quality of solution and running time.

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Ant Colony Optimization is a metaheuristic used to create heuristic algorithms to find good solutions for combinatorial optimization problems. This metaheuristic is inspired on the effective behavior present in some species of ants of exploring the environment to find and transport food to the nest. Several works have proposed using Ant Colony Optimization algorithms to solve problems such as vehicle routing, frequency assignment, scheduling and graph coloring. The graph coloring problem essentially consists in finding a number k of colors to assign to the vertices of a graph, so that there are no two adjacent vertices with the same color. This paper presents the hybrid ColorAnt-RT algorithms, a class of algorithms for graph coloring problems which is based on the Ant Colony Optimization metaheuristic and uses Tabu Search as local search. The experiments with ColorAnt-RT algorithms indicate that changing the way to reinforce the pheromone trail results in better results. In fact, the results with ColorAnt-RT show that it is a promising option in finding good approximations of k. The good results obtained by ColorAnt-RT motivated it use on a register allocation based on Ant Colony Optimization, called CARTRA. As a result, this paper also presents CARTRA, an algorithm that extends a classic graph coloring register allocator to use the graph coloring algorithm ColorAnt-RT. CARTRA minimizes the amount of spills, thereby improving the quality of the generated code.

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