A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.

Jenetics - Genetic Algorithm, Genetic Programming, Grammatical Evolution, Evolutionary Algorithm, and Multi-objective Optimization

A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.

Code for the paper "Evolved Policy Gradients"

ecr: Evolutionary Computation in R (version 2)

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

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

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

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

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

This github repository contains the official code for the paper, "Evolving Robust Neural Architectures to Defend from Adversarial Attacks"

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

Workbench for practical machine learning in Ruby.

A tool for developing reinforcement learning algorithms focused in stock prediction

[WIP] Python implementation of evolution strategy based on Information Geometry. This library includes CMA-ES, NES, CompactGA and PBIL.

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.

An amateur attempt at breeding a chess-playing AI.

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.

Tiny Genetic Algorithm in Python.

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.

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Esta aplicação fornece uma interface web a fim de demonstrar o uso do Algoritmo de colonização de formigas Antsystem

JADE - Adaptive Differential Evolution

Implementation of EDCA-Net published in International Journal of Neural System.

evolutionary-based approach in RBF neural network training

First assignment for Evolutionary Computing class at @vrije-universiteit-amsterdam

Generic implementation of genetic algorithm in Java.

Blender/Bullet automatic parameter tuning/learning.

Small experiments on MNIST to evaluate ES and GA against SGD

Atari AI Agents powered by Natural Evolution Strategies