Reverse Dependencies of deap
The following projects have a declared dependency on deap:
- addm-toolbox — A toolbox for data analysis using the attentional drift-diffusion model.
- antco — Ant Colony Optimization framework
- Autofhm — Python Automated Machine Learning
- autogenes — Automatic Gene Selection for Bulk Deconvolution
- autoqtl — Automated Quantitative Trait Locus Analysis Tool
- bandit-optimization — Bandit optimization algorithms for microscopy
- bluepyopt — Bluebrain Python Optimisation Library (bluepyopt)
- BOFdat — Package to generate biomass objective function stoichiometric coefficients from experimental data
- casm-python — CASM Python interface, tools, and wrappers.
- causal-testing-framework — A framework for causal testing using causal directed acyclic graphs.
- cdsaxs — package streamlines CD-SAXS data analysis by combining model generation, optimization, and uncertainty estimation for nanostructure characterization
- chem-ant — Select materials to output molecules similar to the target molecule with MCTS Solver and Genetic Programming.
- chess-ant — Simulator to solve chess problems with MCTS Solver and Genetic Programming.
- CompNeuroPy — General package for computational neuroscience with ANNarchy.
- creamas — A library for creative MAS build on top of aiomas.
- DDFacet — Facet-based radio astronomy continuum imager
- deap-misl — DEAP additional tooklit by MISL
- deatf — Distributed Evolutionary Algorithms in TensorFlow (DEATF) is a framework where networks generated with TensorFlow are evolved via DEAP.
- DeepGProp — Train Multilayer Perceptrons with Genetic Algorithms.
- diego — Diego: Data IntElliGence Out.
- digen — DIGEN: Diverse Generative ML Benchmark
- digneapy — Python version of the DIGNEA code for instance generation
- discrete-optimization — Discrete optimization library
- DrugTargetCom — Genetic Algorithm Based Combination Therapy Finder
- easy-geppy — EasyGeppy is an easy to use programming interface for Geppy
- eecr — Tools for the optimization of energy in a context recognition task
- elm — Python Extreme Learning Machine (ELM) is a machine learning technique used for classification/regression tasks.
- embarrassingly — Pimp your objective function for faster, robust optimization
- epct — Evolutionary approach to constructing PCT hierarchies
- eptune — Evolutionary parameter tuning
- evocov — GPlib extension to learn the kernel function.
- evolutionary-forest — An open source python library for automated feature engineering based on Genetic Programming
- EvolutionaryModelDiscovery — EvolutionaryModelDiscovery: Automated agent rule generation and importance evaluation for agent-based models with Genetic Programming.
- EZFF — Multiobjective forcefield optimization for Molecular Dynamics
- FAPSDemonstratorAPI — FAPS Demonstrator FAPSDemonstratorAPI api
- fast-machine-learning — fast-machine-learning
- feature-selection-ga — Feature Selection using Genetic Algorithm (DEAP Framework)
- ga-optimization — A package for genetic algorithm-based feature selection on Random Forest Classifier
- gadgit — Genetic Algorithm for Disease Gene Identification Toolbox
- geb — GEB simulates the environment, the individual behaviour of people, households and organizations - including their interactions - at small and large scale.
- gena-selector — Feature selection with genetic algorithm.
- GENDIS — Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.
- genens — A genetic AutoML system for ensemble methods
- geneticpotion — Linear Classificator enhancer based on genetic algorithm
- GIMMECore — GIMME (Group Interactions Management for Multiplayer sErious games) is a research tool which focuses on the management of interactions in groups so that the collective ability improves.
- glas — Python module for solving the inverse design of materials
- gpr-algorithm — Gene Programming Rules (GPR) implementation
- grape-mathlab — GRAph Parallel Environment.
- hawks — A package for generating synthetic clusters, with parameters to customize different aspects of the complexity of the cluster structure
- hetrob — A python package for the optimzation of schedules for heterogeneous, cooperating robot teams
- hpo-uq — Hyperparameter Optimization Tool using Surrogate Modeling and Uncertainty Quantification.
- hydrocnhs — A Python Package of Hydrological Model for Coupled Natural–Human Systems.
- hydromodel — hydrological models starting from XinAnJiang
- jcvi — Python utility libraries on genome assembly, annotation and comparative genomics
- kinetics — Python code to run kinetic models of enzyme reactions
- LORE-ext — LORE (LOcal Rule-based Explanations) is a model-agnostic explanator for tabular data
- LycorisAD — An elegant outlier detection algorithm framework based on AutoEncoder.
- metaheuristic — A package with some metaheuristics to feature selection
- metku — Module for structural analysis and optimization
- mggp — Multi Gene Genetic Programming toolbox for System Identification
- MGSurvE — MGSurvE
- microservice-story-manager — Microservice Story Manager is a Python library for optimizing user story assignments in microservice architectures. It features story and microservice management, genetic algorithm optimization, and visualization tools. The library calculates metrics like coupling and cohesion, allows comparison of optimization strategies, and supports CSV data loading. It aids in microservice design decisions, helping maximize cohesion and minimize coupling in software architectures.
- midi-generator — MIDI Generator for Python using genetic algorithms
- mlo-optimizer — no summary
- mloptimizer — mloptimizer is a Python library for optimizing hyperparameters of machine learning algorithms using genetic algorithms.
- nemopt — National Electricity Market Optimiser
- neuroflex — An advanced neural network framework with interpretability, generalization, robustness, and fairness features
- olymp — Benchmarking framework for noisy optimization and experiment planning
- ontolearn — Ontolearn is an open-source software library for structured machine learning in Python. Ontolearn includes modules for processing knowledge bases, inductive logic programming and ontology engineering.
- ontosample — Ontosample is a package that offers different sampling techniques for OWL ontologies.
- opensbt — OpenSBT is a Modular Framework for Search-based Testing of Automated Driving Systems
- osier — osier: A justice oriented energy system optimization tool
- PowerNovo — PowerNovo: A New Efficient Tool for De Novo Peptide Sequencing
- ProcessOptimizer — Sequential model-based optimization toolbox (forked from scikit-optimize)
- prodsys — A useful module for production system simulation and optimization
- pstree — An open source python library for non-linear piecewise symbolic regression based on Genetic Programming
- pybrops — Python package for breeding program numerical optimization and simulation
- pyglyph — Symbolic regression tools.
- pyrea — Multi-view clustering with deep ensemble structures.
- pyrfume — A validation library for human olfactory psychophysics research.
- PyXRD — PyXRD is a python implementation of the matrix algorithm developed for the X-ray diffraction analysis of disordered lamellar structures
- qdpy — Quality-Diversity algorithms in Python
- qsarmodelingpy — A package for building and validating QSAR models
- quantum-tree — Quantum decision trees with binary features and binary classes
- RapidML — RapidML is your Smart Machine Learning assistant that not only automates the creation of machine learning models but also enables you to easily deploy the models to the cloud. Find the documentation at: https://ritabratamaiti.github.io/RapidML
- rheia — Robust design optimization of renewable Hydrogen and dErIved energy cArrier systems
- rheia-meca2675 — Robust design optimization of renewable Hydrogen and dErIved energy cArrier systems
- rollo — Reactor Evolutionary Algorithm Optimizer
- sampo — Open-source framework for adaptive manufacturing processes scheduling
- secml-malware — no summary
- setga — library designed to extract a minimal subset from a given set, optimizing a given (set of) objective(s). Based on the DEAP library.
- sklearn-deap — Use evolutionary algorithms instead of gridsearch in scikit-learn.
- sklearn-deap2 — Use evolutionary algorithms instead of gridsearch in scikit-learn.
- sklearn-genetic — Genetic feature selection module for scikit-learn
- sklearn-genetic-opt — Scikit-learn models hyperparameters tuning and features selection, using evolutionary algorithms
- skpar — Optimisation of Slater-Koster files (.skf) for density functional-based tight binding (DFTB)
- slrkit — Tools to automatize systematic literature reviews
- spiegelib — Synthesizer Programming with Intelligent Exploration, Generation, and Evaluation Library
- SRI-TPOT — SRI Fork of Tree-based Pipeline Optimization Tool
- SRITPOT — SRI Fork of Tree-based Pipeline Optimization Tool
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