evolutionary-strategy
There are 64 repositories under evolutionary-strategy topic.
jenetics/jenetics
Jenetics - Genetic Algorithm, Genetic Programming, Grammatical Evolution, Evolutionary Algorithm, and Multi-objective Optimization
esa/pagmo2
A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
esa/pygmo2
A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
openai/EPG
Code for the paper "Evolved Policy Gradients"
aliasgharheidaricom/Harris-Hawks-Optimization-Algorithm-and-Applications
Source codes for HHO paper: Harris hawks optimization: Algorithm and applications: https://www.sciencedirect.com/science/article/pii/S0167739X18313530. In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO).
jakobbossek/ecr2
ecr: Evolutionary Computation in R (version 2)
NiMlr/High-Dim-ES-RL
Paper: Challenges in High-dimensional Reinforcement Learning with Evolution Strategies
scheckmedia/cgp-cnn-design
Using Cartesian Genetic Programming to find an efficient Convolutional Neural Network architecture
sash-a/es_pytorch
High performance implementation of Deep neuroevolution in pytorch using mpi4py. Intended for use on HPC clusters
sgonzalez/SwiftCMA
A pure-Swift implementation of Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES).
entrpn/fingym
A tool for developing reinforcement learning algorithms focused in stock prediction
AStupidBear/GCMAES.jl
Gradient-based Covariance Matrix Adaptation Evolutionary Strategy for Real Blackbox Optimization
giuse/machine_learning_workbench
Workbench for practical machine learning in Ruby.
shashankkotyan/RobustArchitectureSearch
This github repository contains the official code for the paper, "Evolving Robust Neural Architectures to Defend from Adversarial Attacks"
satuma777/evoltier
[WIP] Python implementation of evolution strategy based on Information Geometry. This library includes CMA-ES, NES, CompactGA and PBIL.
MarkZH/Genetic_Chess
An amateur attempt at breeding a chess-playing AI.
moshesipper/tiny_ga
Tiny Genetic Algorithm in Python
underwit/agentsmith
Нейронная сеть оптимизируемая с помощью генетического алгоритма. Задача агента контролируемого при помощи нейронной сети состоит в том, чтобы избегать контакта с противниками, как можно более длительное время.
sergio94al/Automatic_design_of_quantum_feature_maps_Genetic_Auto-Generation
Registered Software. Official code of the published article "Automatic design of quantum feature maps". This quantum machine learning technique allows to auto-generate quantum-inspired classifiers by using multiobjetive genetic algorithms for tabular data.
ekorudiawan/JADE
JADE - Adaptive Differential Evolution
SergioNoivak/GraphicalAnt.js
Esta aplicação fornece uma interface web a fim de demonstrar o uso do Algoritmo de colonização de formigas Antsystem
aliarjomandbigdeli/RBF_net_evolutionary_training
evolutionary-based approach in RBF neural network training
aliasgharheidaricom/RUN-Beyond-the-Metaphor-An-Efficient-Optimization-Algorithm-Based-on-Runge-Kutta-Method
The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification methods on easy problems, and high similarity between their components' interactions. This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics. The proposed RUNge Kutta optimizer (RUN) was developed to deal with various types of optimization problems in the future. The RUN utilizes the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization. This search mechanism benefits from two active exploration and exploitation phases for exploring the promising regions in the feature space and constructive movement toward the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid the local optimal solutions and increase convergence speed. The RUN algorithm's efficiency was evaluated by comparing with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN provided very promising and competitive results, showing superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its suitable performance as well. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization. The source codes, supplementary materials, and guidance for the developed method will be publicly available at different hubs at http://aliasgharheidari.com/RUN.html.
GoingMyWay/NES-Agents
Atari AI Agents powered by Natural Evolution Strategies
lettier/bbautotune
Blender/Bullet automatic parameter tuning/learning.
AmineAmarir/GenericGeneticAlgorithm
Generic implementation of genetic algorithm in Java.
hengdezhu/EDCA-Net
Implementation of EDCA-Net published in International Journal of Neural System.
itssamuelrowe/Helix
OneCube Evolve is a simple genetic algorithm library written in Java.
LevinaLab/evolution_dynamical_regime
Which dynamical regime is beneficial for biological systems in the context of the criticality hypothesis? Agent-based evolutionary foraging game with experiments to evaluate generalizability, ability to perform complex tasks and evolvability of agents with respect to their dynamical regime. Paper: https://arxiv.org/abs/2103.12184
nihalsid/deep-learning-experiments
Small experiments on MNIST to evaluate ES and GA against SGD
TeamEightyEight/Assignment1
First assignment for Evolutionary Computing class at @vrije-universiteit-amsterdam
vkurenkov/bcr-project
Experiments with Guided Evolutionary Strategies for Behavioral Robotics course project at Innopolis Univeristy
marcocolangelo/Computational-Intelligence
The content of this repository will be inherent to the Computational Intelligence course at Polytechnic University of Turin academic year 2023/2024
roaked/snake-evolutionary-reinforcement-learning
parameter optimization of a reinforcement learning deep Q network with memory replay buffer using genetic algorithm in the snake game. base code for snake env from codecamp