evolutionary-strategy

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

Harris Hawks Optimization (HHO) is a nature-inspired metaheuristic algorithm that simulates the cooperative hunting behavior of Harris' hawks. Widely used in engineering, machine learning, and resource allocation, HHO is renowned for its simplicity, versatility, and effectiveness in finding global optima.

jakobbossek/ecr2

ecr: Evolutionary Computation in R (version 2)

jbrea/CMAEvolutionStrategy.jl

A julia implementation of the CMA Evolution Strategy for derivative-free optimization of potentially non-linear, non-convex or noisy functions over continuous domains.

NiMlr/High-Dim-ES-RL

Paper: Challenges in High-dimensional Reinforcement Learning with Evolution Strategies

sash-a/es_pytorch

High performance implementation of Deep neuroevolution in pytorch using mpi4py. Intended for use on HPC clusters

scheckmedia/cgp-cnn-design

Using Cartesian Genetic Programming to find an efficient Convolutional Neural Network architecture

sgonzalez/SwiftCMA

A pure-Swift implementation of Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES).

giuse/machine_learning_workbench

Workbench for practical machine learning in Ruby.

AStupidBear/GCMAES.jl

Gradient-based Covariance Matrix Adaptation Evolutionary Strategy for Real Blackbox Optimization

entrpn/fingym

A tool for developing reinforcement learning algorithms focused in stock prediction

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.

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.

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.

AshwiniDPrabhu/Evolutionary-Machine-Learning

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.

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

lettier/bbautotune

Blender/Bullet automatic parameter tuning/learning.

AmineAmarir/GenericGeneticAlgorithm

Generic implementation of genetic algorithm in Java.

GoingMyWay/NES-Agents

Atari AI Agents powered by Natural Evolution Strategies

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