Self-Weighted Optimization: Tree Searches and Character State Reconstructions under Implied Transformation Costs

Area of Science:

• Phylogenetics
• Computational Biology
• Evolutionary Biology

Background:

• Current phylogenetic optimization methods often use linear functions to assess the cost of character state transformations.
• These methods may not accurately reflect the complexity or reliability of evolutionary transformations.
• A need exists for more robust optimality criteria in phylogenetic tree reconstruction.

Purpose of the Study:

• To propose a novel method for evaluating the cost of character state transformations based on congruence.
• To introduce a new optimality criterion that minimizes distortion across all transformations.
• To develop a non-iterative approach for phylogenetic optimization with dynamic cost determination.

Main Methods:

• Assessing transformation costs using a convex increasing function of the number of transformations.
• Minimizing distortion across all transformations to select optimal reconstructions.
• Implementing dynamic determination of transformation costs during tree reconstructions.
• Describing algorithms to reduce computational cost for optimization and searches.

Main Results:

• The proposed method provides a potentially superior optimality criterion compared to linear functions.
• Dynamic cost determination leads to selecting trees with more reliable state transformations.
• The method is non-iterative, ensuring consistent results regardless of starting points.
• Explicit optimality criterion is established.

Conclusions:

• The new method offers a more reliable and explicit approach to phylogenetic optimization.
• Dynamic assessment of transformation costs enhances the accuracy of evolutionary reconstructions.
• Further algorithmic development is needed to mitigate the high computational cost.