A multi-modal algorithm based on an NSGA-II scheme for phylogenetic tree inference.

Area of Science:

• Evolutionary biology
• Computational biology
• Bioinformatics

Background:

• Analyzing evolutionary data reveals organism adaptation and relationships.
• Estimating evolutionary history relies on computational methods, with optimization approaches showing promise.
• Multiobjective optimization offers a powerful framework for complex phylogenetic inference problems.

Purpose of the Study:

• To develop and evaluate a novel multi-modal metaheuristic approach for multiobjective phylogenetic inference.
• To introduce and integrate a new topological tree distance metric into the optimization process.
• To assess the performance of the proposed method against state-of-the-art algorithms and analyze a real-world biological dataset.

Main Methods:

• Utilized a multi-modal metaheuristic algorithm tailored for multiobjective optimization.
• Incorporated a new metric based on topological tree distance to evaluate phylogenetic solutions.
• Compared the proposed method with existing algorithms using performance metrics and conducted a case study on Saccharomyces cerevisiae data.

Main Results:

• The new method demonstrated improved diversity of solutions compared to existing algorithms.
• The approach maintained or improved the quality of solutions, as measured by hypervolume.
• The case study on yeast data validated the practical applicability and effectiveness of the proposed method.

Conclusions:

• The developed multi-modal metaheuristic approach offers an effective strategy for multiobjective phylogenetic inference.
• The integration of a topological tree distance metric enhances the exploration of the solution space.
• This work contributes to advancing computational methods for understanding evolutionary history and organismal relationships.