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Current efforts in solving computationally demanding optimization problems in bioinformatics rely on the combination of bioinspired computing and parallelism. The multiobjective nature shown by a wide variety of these problems represents an additional challenge, as the optimization of multiple objective functions involves growing computational requirements. In this work, a multiobjective metaheuristic...
Throughout the years, biological processing demands have been addressed by relying on the design of algorithmic approaches for parallel architectures. By taking advantage of multicore processor systems, we can deal with the main sources of complexity which explain the NP-hard nature of multiple problems in computational biology. In this work, we address the inference of phylogenetic topologies by...
The development of increasingly popular multiobjective metaheuristics has allowed bioinformaticians to deal with optimization problems in computational biology where multiple objective functions must be taken into account. One of the most relevant research topics that can benefit from these techniques is phylogenetic inference. Throughout the years, different researchers have proposed their own view...
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