EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration

• URL: http://arxiv.org/abs/2207.10367v2
• Date: Wed, 19 Apr 2023 12:05:44 GMT
• Title: EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration
• Authors: Moshe Sipper, Tomer Halperin, Itai Tzruia, Achiya Elyasaf
• Abstract summary: EC-KitY is a Python library for doing evolutionary computation. This paper provides an overview of the package, including the ease of setting up an EC experiment.
• Score: 0.8602553195689513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under the BSD 3-Clause License, and compatible with scikit-learn. Designed with modern software engineering and machine learning integration in mind, EC-KitY can support all popular EC paradigms, including genetic algorithms, genetic programming, coevolution, evolutionary multi-objective optimization, and more. This paper provides an overview of the package, including the ease of setting up an EC experiment, the architecture, the main features, and a comparison with other libraries.

Related papers

• A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning [42.350737545269105]
  We show how to run Python's scikit-learn, pytorch and OpenAI gym libraries for building Machine Learning, Deep Learning, and Reinforcement Learning projects easily.
  arXiv  Detail & Related papers  (2024-07-19T23:01:48Z)
• eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles [3.465746303617158]
  eipy is an open-source Python package for developing effective, multi-modal heterogeneous ensembles for classification. eipy provides both a rigorous, and user-friendly framework for comparing and selecting the best-performing data integration and predictive modeling methods.
  arXiv  Detail & Related papers  (2024-01-17T20:07:47Z)
• sQUlearn -- A Python Library for Quantum Machine Learning [0.0]
  sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML) The library's dual-layer architecture serves both QML researchers and practitioners.
  arXiv  Detail & Related papers  (2023-11-15T14:22:53Z)
• LCE: An Augmented Combination of Bagging and Boosting in Python [45.65284933207566]
  lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. Local Cascade Ensemble (LCE) is a machine learning method that further enhances the prediction performance of the current state-of-the-art methods Random Forest and XGBoost.
  arXiv  Detail & Related papers  (2023-08-14T16:34:47Z)
• PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series [0.0]
  PyPOTS is an open-source Python library dedicated to data mining and analysis on partially-observed time series. It provides easy access to diverse algorithms categorized into four tasks: imputation, classification, clustering, and forecasting.
  arXiv  Detail & Related papers  (2023-05-30T07:57:05Z)
• EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation [40.71953374838183]
  EvoX is a computing framework tailored for automated, distributed, and heterogeneous execution of EC algorithms. At the core of EvoX lies a unique programming model to streamline the development of parallelizable EC algorithms. EvoX offers comprehensive support for a diverse set of benchmark problems, ranging from dozens of numerical test functions to hundreds of reinforcement learning tasks.
  arXiv  Detail & Related papers  (2023-01-29T15:00:16Z)
• Scikit-dimension: a Python package for intrinsic dimension estimation [58.8599521537]
  This technical note introduces textttscikit-dimension, an open-source Python package for intrinsic dimension estimation. textttscikit-dimension package provides a uniform implementation of most of the known ID estimators based on scikit-learn application programming interface. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation in real-life and synthetic data.
  arXiv  Detail & Related papers  (2021-09-06T16:46:38Z)
• Extending Python for Quantum-Classical Computing via Quantum Just-in-Time Compilation [78.8942067357231]
  Python is a popular programming language known for its flexibility, usability, readability, and focus on developer productivity. We present a language extension to Python that enables heterogeneous quantum-classical computing via a robust C++ infrastructure for quantum just-in-time compilation.
  arXiv  Detail & Related papers  (2021-05-10T21:11:21Z)
• PyHealth: A Python Library for Health Predictive Models [53.848478115284195]
  PyHealth is an open-source Python toolbox for developing various predictive models on healthcare data. The data preprocessing module enables the transformation of complex healthcare datasets into machine learning friendly formats. The predictive modeling module provides more than 30 machine learning models, including established ensemble trees and deep neural network-based approaches.
  arXiv  Detail & Related papers  (2021-01-11T22:02:08Z)
• Pythonic Black-box Electronic Structure Tool (PyBEST). An open-source Python platform for electronic structure calculations at the interface between chemistry and physics [52.77024349608834]
  Pythonic Black-box Electronic Structure Tool (PyBEST) was developed at Nicolaus Copernicus University in Toru'n. PyBEST is written primarily in the Python3 programming language with additional parts written in C++. The capability of PyBEST to perform large-scale electronic structure calculations is demonstrated for the model vitamin B12 compound.
  arXiv  Detail & Related papers  (2020-10-12T07:10:23Z)
• DIETERpy: a Python framework for The Dispatch and Investment Evaluation Tool with Endogenous Renewables [62.997667081978825]
  DIETER is an open-source power sector model designed to analyze future settings with very high shares of variable renewable energy sources. It minimizes overall system costs, including fixed and variable costs of various generation, flexibility and sector coupling options. We introduce DIETERpy that builds on the existing model version, written in the General Algebraic Modeling System (GAMS) and enhances it with a Python framework.
  arXiv  Detail & Related papers  (2020-10-02T09:27:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.