In the study of evolutionary biology, there exists a method called the “cherry picking algorithm” that produces the instructions needed to create a network that shows how different species are related.
This report explores what happens when the algorithm starts with a wrong choice, or a “suboptimal cherry” for the first step of the algorithm, and how this affects the accuracy of the algorithm. Imagine you are trying to build a family tree for different species, but you start with a mistake. This research looks at how such initial mistakes can impact the accuracy of the entire family tree. We conducted this study
using simulations of the algorithm that deliberately make an initial mistake, and afterwards continue as the algorithm would normally. The study found that starting with a wrong step in the algorithm usually makes the performance of the algorithm worse. Specifically, it lead to an average optimal performance decrease of 34,8% for networks relating a smaller number of species, and 11.3% for networks relating a larger number of species. Interestingly, the larger the number of species we are attempting to relate in the network produced by our algorithm, the less severe the impact of the initial mistake.
We concluded that making an initial mistake negatively effects the average performance of the algorithm, and the extent of the effect varies with the number of species we are trying to relate in our network.

The field of robust optimization deals with problems where uncertainty influences the optimal decision. Some of these problems can be formulated in a ‘two-stage’ formulation, such as the location transportation problem. To solve such a problem, a column-and-constraint-generation algorithm has been introduced in which constraints are iteratively added to mixed-integer pro- gram based on different uncertain scenarios. However, if these scenarios are randomly chosen, this problem can grow too large to efficiently solve for. For most problems, there is some mini- mal set of scenarios needed to find the optimal solution, and it is important to find the ‘right’ scenarios early. In this study, we attempt to predict these scenarios for a location transportation using machine learning. Using customer demand data for different instances of the problem, we train a logistic regression classifier, a neural network and a random forest classifier to predict important scenarios for newly generated problems. We find that when applying these machine learning tools, we reach an average reduction of scenarios added to the problem ranging from 8% to 24%. Even though we do not spend much effort on training perfect models, we see that there is a strong indication that machine learning can be used to increase the efficiency of the algorithm.