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EVO-ML Research Group

EVO-ML Research Group
To add a function to EvoloPy 1. Define the function in the 'benchmark.py' file. 2. Add the function details (name, lower bound, upper bound, and dimension) to the 'param' dictionary at the 'getFunctionDetails' function at the 'benchmark.py' file 3. Use the function by adding its name to the 'objectivefunc' list at the 'example.py' file
An efficient evolutionary algorithm with a nearest neighbor search technique for clustering analysis 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. Useful links GitHub: https://github.com/RaneemQaddoura/EvoNP Google colab: https://github.com/RaneemQaddoura/EvoNP/blob/master/examples/example0.ipynb & https://github.com/RaneemQaddoura/EvoNP/blob/master/examples/example1.ipynb Documentation of the source code: http://evo-ml.com/evonp-documentation/ Published Paper: https://link.springer.com/article/10.1007/s12652-020-02570-2
An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio Nearest Point with Indexing Ratio (NPIR) is a clustering algorithm that explores the characteristics of the data points to group similar data points into the same clusters and dissimilar data points into different clusters. It is based on the nearest neighbor search technique in finding a k-nearest neighbor to a certain point. The algorithm iterates to assign data points to the most suitable clusters. It performs Election, Selection, and Assignment operations to assign data points to appropriate clusters. Useful links GitHub: https://github.com/RaneemQaddoura/NPIR Google colab: https://github.com/RaneemQaddoura/NPIR/blob/master/example1.ipynb & https://github.com/RaneemQaddoura/NPIR/blob/master/example2.ipynb Documentation of the source code: http://evo-ml.com/npir-documentation/ Live demo: http://evo-ml.com/NPIR-LIVE-DEMO/ Published Paper: https://doi.org/10.1007/s13042-019-01027-z
As an initiative to keep an 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 a wide range of classical and recent metaheuristic algorithms. The goal of the framework is to take advantage of the rapidly growing scientific community of Python and provide a set of robust optimizers as free and open source software. We believe that implementing such algorithms in Python will increase their popularity and portability among researchers and non-specialists coming from different domains. The powerful libraries and packages available in Python (such as NumPy) will make it more feasible to apply metaheuristic algorithms for solving complex problems on a much higher scale. EvoloPy facilitates designing new algorithms or improving, hybridizing, and analyzing the current ones. Github: https://github.com/7ossam81/EvoloPy Google colab: https://github.com/7ossam81/EvoloPy/blob/master/EvoloPy.ipynb Paper: https://www.scitepress.org/Papers/2016/60482/60482.pdf
This is a demo of how to use EvoCluster framework at GitHub and google Colab. 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 customizable implementation of the metaheuristic based clustering algorithms which can be utilized by experienced and non-experienced users for different applications. The current implementation of the framework includes ten metaheuristic optimizers, thirty datasets, five objective functions, and twelve evaluation measures. Github: https://github.com/RaneemQaddoura/EvoCluster Google colab: https://github.com/RaneemQaddoura/EvoCluster/blob/master/EvoCluster.ipynb evo-ml web page: http://evo-ml.com/EvoCluster Paper: https://link.springer.com/chapter/10.1007/978-3-030-43722-0_2
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 customizable implementation of the metaheuristic based clustering algorithms which can be utilized by experienced and non-experienced users for different applications. The framework can also be used by researchers who can benefit from the implementation of the metaheuristic optimizers for their research studies. EvoCluster can be extended by designing other optimizers, including more objective functions, adding other evaluation measures, and using more data sets. The current implementation of the framework includes ten metaheuristic optimizers, thirty datasets, five objective functions, and twelve evaluation measures. Useful Links The source code can be found on GitHub (http://evo-ml.com/2019/10/25/evocluster/) The Google colab copy (https://github.com/RaneemQaddoura/EvoCluster/blob/master/EvoCluster.ipynb) The documentation of the source code (http://evo-ml.com/evocluster-source-code-documentation/) Published Paper (https://link.springer.com/chapter/10.1007/978-3-030-43722-0_2)
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