There are 4 repositories under deap topic.
Feature Selection using Genetic Algorithm (DEAP Framework)
ML hyperparameters tuning and features selection, using evolutionary algorithms.
CNN architecture exploration using Genetic Algorithm
A Python implementation of the decomposition based multi-objective evolutionary algorithm (MOEA/D)
Emotional Video to Audio Transformation with ANFIS-DeepRNN (Vanilla RNN and LSTM-DeepRNN) [MPE 2020]
A project on improving Neural Networks performance by using Genetic Algorithms.
Kaggle LANL Earthquake Prediction challenge, Genetic Algorithm (DEAP) + CatboostRegressor, private score 2.425 (31 place)
A stochastic circuit optimizer for Cadence Virtuoso, using the NSGA-II genetic algorithm.
Genetic algorithm to solve modular exams scheduling problem written in Python.
[ICASSP 2022] EEG - Music Cross Modal Learning
Evolutionary Composition uses genetic algorithms to create and enhance musical melodies.
Code for my Master's Thesis at the Institute of Medical Informatics, Universität zu Lübeck.
Motif discovery for DNA sequences using multiobjective optimization and genetic programming.
Playing around with DEAP's genetic programming to generate a regex that matches everything in one list and nothing in another list
( 🦉 ) This code lab intended to introduce new Machine Learning Algorithm // DEAP : Distributed Evolutionary Algorithm Framework.
Final Year project on Multi Objective Multi robot task allocation using Genetic Algorithms. It explores qualitatively and quantitatively balancing conflicts when optimising tasks scheduling for time and distance in a logistics scenario.
Evolutionary algorithm for neural network structure
Seminar project at FER led by Assistant Professor Marko Čupić
Harness the power of Genetic Algorithms to optimize vehicle routes for the Vehicle Routing Problem (VRP) with this Python-based solution. Achieve up to 15% reduced travel distance and 20% improved delivery efficiency using the DEAP library for evolutionary computation. Visualize your results with Matplotlib for clear, data-driven decisions. 📈🧬
Thesis activity carried out during the laboratory internship at the University of Parma, the goal is to generate filters for denoise and edge detection using genetic programming.
Using Genetic programming, to evolve stategies that preditors and preys will use in a simulated environment, such that Preditors get better at catching preys over time (generations).
Genetic Algorithm as an approach to select image restoration parameters that provides the greatest improvement to a turbid underwater image.