EvoCC framework is an open-source, free, and cross-platform framework implemented in Python which combines clustering, classification, and evolutionary computation methods. It optimizes the classification process by generating a classification model for each group generated by a clustering process where the …
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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. …
Feature selection is a necessary critical stage in data mining process. There is always an arm race to build frameworks and libraries that ease and automate this process. In this chapter, an EvoloPy-FS framework is proposed, which is a Python …
Nearest Point with Indexing Ratio (NPIR) tries to solve the clustering problem based on the nearest neighbor search technique by finding the nearest neighbors for the points that are already clustered based on the distance between them and cluster them …
EvoCluster is an open source and cross-platform framework implemented in Python which includes the most well-known and recent nature-inspired metaheuristic optimizers that are customized to perform partitional clustering tasks. The goal of this framework is to provide a user-friendly and …
HHO is a population-based, gradient-free optimization algorithm. In 2019, Future Generation Computer Systems (FGCS) has published the HHO algorithm. The main inspiration of HHO is the cooperative behavior and chasing style of Harris’ hawks in nature called “surprise pounce”.
As an initiative to keep the implementation of the recent nature-inspired metaheuristics as well as the classical ones in a single open-source framework, we introduce EvoloPy. EvoloPy is an open-source and cross-platform Python framework that implements various classical and recent …