EvoNP is a clustering algorithm which aims at grouping similar data points to the same cluster and dissimilar data points to different clusters. It is based on the evolution behavior of genetic algorithm and the Nearest Neighbor Search (NNS) technique.

The algorithm starts by reading the data set and generating the initial population. The initial population is then passed to the Nearest Point (NP) clustering technique where the Election, Selection, and Assignment operators are generating an updated population which is then evaluated using a specific fitness function. The population is then passed to the evolutionary operators where the selection, crossover, mutation, and elitism operators are performed generating an evolved population. The evolved population is then considered as a new population for the next round of evolving using the NP clustering technique and evolutionary operators until a predefined number of generations is reached. The best chromosome from the last generation is considered as the final best solution.

Team Members:

Useful Links:

• The source code of EvoNP can be found on GitHub here
• The Google colab copy can be found here and here
• The documentation of the source code can be found here
• Published Paper of EvoNP can be found here

Published Paper: