Multiple problems in bioinformatics research involve the optimization of time-consuming objective func-tions over exponentially growing search spaces. The capa-bilities shown by modern parallel systems composed of clustered multicore multiprocessors represent an opportunity to address such difficult problems. A suitable paradigm to exploit these systems lies on the combination of mixed mode programming and evolutionary computation. This research
focuses on the reconstruction of multiobjective phylogenetic hypotheses by using an indicator-based evolutionary algo-rithm. In order to overcome the main sources of complexity of the problem, we propose a parallel adaptation of thisalgorithm based on master–worker principles. Experimental results on six real data sets report that the design achieves an efficient exploitation of a shared–distributed memory hybrid system composed of 48 processing cores, observing improved scalability in comparison with other parallel pro-posals. In addition, the inferred Pareto fronts give account of the relevance of the indicator-based design, verifying signif-icant solution quality under different multiobjective metrics and biological testing procedures.

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