In biological systems, an individual's physical traits are encoded in its DNA, which is unique to each individual, and contain information from both of its parents. During reproduction, parts of the DNA from both parents crossover to form a new sequence for the child. During this process, there is a chance a gene may undergo mutation and lead to a different trait. Similarly in GAs, each individual solution has unique sets of genes that contain information from parents in the previous generation. Each individual's genes need to be represented computationally in a way that allows for efficient crossover and mutation. The simplest representation is binary strings, where each position codes for a trait. In chemical applications, it is difficult to maintain all relevant molecular information with this strategy. The most common representations for chemical GAs are molecular-based strings such as SMILES and SELFIES , or molecular graphs where the nodes are atoms and the edges are bonds . SMILES-based representations commonly use a fragment-based approach, using a library of SMILES fragments to mix and match and design new molecules. One drawback to this method is that it places more limits on molecular design and therefore on the search space. Alternatively, performing random mutations to a SELFIES string will always maintain chemical validity and allow for near-infinite possibilities. This representation has already been incorporated into successful GAs, allowing for the insertion, deletion, and replacement of characters in the string. A more recent technique is the highly successful graph-based GA developed by Jensen 10 that has been shown to quickly explore chemical space with the molecules having little resemblance to the initial population. Since each individual is a graph instead of a string, crossover of parents occurs by cutting each parent into fragments and swapping the fragments to make the new molecule, while ensuring the newly formed children are chemically valid. Graphs can have mutations similar to SELFIES, such as insertions, deletions, and replacing atoms. Additionally, there are opportunities to mutate the bonds, such as changing the order of the bonds or adding a ring bond . In this work, we use fragment-based GAs with SMILES representations because of their simplicity and easily defined search space.