Mauro Henrique Mulati

Register Allocation with Graph Coloring by Ant Colony Optimization

The goal of register allocation is to allocate an unbounded number of program values to a finite number of machine registers. In this paper, we describe a new algorithm for intraprocedural register allocation called CA-RT-RA, an algorithm that extends a classic graph coloring register allocator to use our graph coloring algorithm Color Ant-RT. The experiments demonstrated that our algorithm is able to minimize the amount of spills, thereby improving the quality of the generated code. CA-RT-RA is interesting in applications where compile time is not a concern, but the code quality.

Ant-Line: A Line-Oriented ACO Algorithm for the Set Covering Problem

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.

Toward Better Performance of ColorAnt ACO Algorithm

This paper presents the Color Ant-RT algorithm version 3, an algorithm for graph coloring problems which is based on the Ant Colony Optimization metaheuristic and uses Tabu Search as local search. The experiments demonstrated that ColorAnt3-RT is a promising option in finding good approximations to the best known results for geometric random, geometric standard and le450 graphs of DIMACS benchmark in an acceptable runtime, also, it is good in minimizing the amount of conflicts, the main problem of graph coloring with a fixed number of colors.

Register Allocation by Evolutionary Algorithm

Graph coloring is a highly effective approach to intraprocedural register allocation. In this paper, we describe a new algorithm for intraprocedural register allocation called HECRA, an algorithm that extends a classic graph coloring register allocator to use a hybrid evolutionary coloring algorithm. The experiments demonstrated that our algorithm is able to minimize the amount of spills, thereby improving the quality of the generated code. Besides, HECRA is interesting in contexts where compile time is a concern, and not only the quality of the generated code.

AntSeg: The Application of Ant Colony Optimization to Interactive Image Segmentation

The computational process of image segmentation performs the partitioning of it in disjoint regions by the specification and application of specific criteria. This process can be automatic or interactive. The interactive process allows the user interference in this process by means of the insertion of new criteria at any instant of the process execution. This new criterion can lead to the reaching of more appropriate results for the solution to the problem. However, interactive process application requires the user to have a high ability, context knowledge and accuracy executing the tasks. The interactive process can include facilities by allowing that some user actions may be executed with relative accuracy. Using computational resources, such task is made easier, since a computer can perform with more accuracy and speed, from the guides interactively defined by the user. The literature presents many methods that perform this task, among them the live wire, intelligent scissors and the interactive watershed with markers. The first two use a minimum cost system, in other words, optimization, to their working. This paper proposes an interactive method applied to image segmentation that combines the artificial ant colonies behavior and the image gradient technique. The gradient, supplied by heuristic rules, is the surface the ants search the path to the desired solution. The segmentation starts with the imposition of an initial point at the border of the object of interest and, by the user adding new points along its border, new segments belonged to object border are conquered. The proposed method shows a high repeatability of the segmentation task.