Update README.md

ErillLab · Jan 3, 2025 · cd386bf · cd386bf
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 ![FLEMINGO Logo](https://github.com/ErillLab/FLEMINGO/blob/main/images/FLEMINGO_logo.jpg)
 
-The idea of this project is to "learn" regulated promoter models, based on a set of user-provided sequences (referred to as the positive set), using a restricted Genetic Programming framework. The _restriction_ in this setup is that the models to be evolved are not open tree topologies (see [previous iteration](https://github.com/ErillLab/TF_GA) of the project), but chains connecting _recognizers_ via binary _connectors_.
+The idea of this project is to "learn" regulated promoter models, based on a set of user-provided sequences (referred to as the positive set), using an evolutionary algorithm. The _restriction_ in this setup is that the models to be evolved are not open tree topologies (see [previous iteration](https://github.com/ErillLab/TF_GA) of the project), but chains connecting _recognizers_ via binary _connectors_.
 
-The GP framework is tasked with evolving organisms that "bind" the positive dataset preferentially over a control dataset (referred to as the negative dataset). Optimal binding energies for the organisms on each sequence are computed using a variant of the Needleman–Wunsch algorithm.
+The algorithm is tasked with evolving organisms that "bind" the positive dataset preferentially over a control dataset (referred to as the negative dataset). Optimal binding energies for the organisms on each sequence are computed using a specific dynamic programming algorithm.