Topic: evolutionary-strategy Goto Github
Some thing interesting about evolutionary-strategy
Some thing interesting about evolutionary-strategy
evolutionary-strategy,Solutions to OpenAI-Gym environments.
User: adriel-m
evolutionary-strategy,This Repository consist of codes which could be used for training RL Bots using Exploration Strategies. It consists of Algorithm such as GA, Hill Climb, Crossover Etc
User: akashkmr27089
evolutionary-strategy,evolutionary-based approach in RBF neural network training
User: aliarjomandbigdeli
evolutionary-strategy,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).
User: aliasgharheidaricom
Home Page: https://aliasgharheidari.com/HHO.html
evolutionary-strategy,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.
User: aliasgharheidaricom
Home Page: https://aliasgharheidari.com/RUN.html
evolutionary-strategy,Implementation of RBF nerual network trained using evolutionary strategy for my computational intelligence final project
User: aliizadi
evolutionary-strategy,Generic implementation of genetic algorithm in Java.
User: amineamarir
evolutionary-strategy,
User: ashwinidprabhu
evolutionary-strategy,A multi agent re-enforcement learning environment for many on many bot fights between space ships
User: aspk
evolutionary-strategy,Gradient-based Covariance Matrix Adaptation Evolutionary Strategy for Real Blackbox Optimization
User: astupidbear
evolutionary-strategy,Projekt na przedmiot Algorytmy Genetyczne
User: bzbikowski
evolutionary-strategy,JADE - Adaptive Differential Evolution
User: ekorudiawan
evolutionary-strategy,A tool for developing reinforcement learning algorithms focused in stock prediction
User: entrpn
Home Page: http://entrpn.com/fingym/
evolutionary-strategy,A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
Organization: esa
Home Page: https://esa.github.io/pagmo2/
evolutionary-strategy,A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
Organization: esa
Home Page: https://esa.github.io/pygmo2/
evolutionary-strategy,Evolutionary strategies for function optimization
User: fayolle
evolutionary-strategy,Workbench for practical machine learning in Ruby.
User: giuse
evolutionary-strategy,Atari AI Agents powered by Natural Evolution Strategies
User: goingmyway
evolutionary-strategy,Genetic algorithms and evolutionary strategy implementation to solve Ackley function
User: gorkemp
evolutionary-strategy,Implementation of EDCA-Net published in International Journal of Neural System.
User: hengdezhu
evolutionary-strategy,Course of Stochastic Optimization in ISAE-SUPAERO
User: houshuanglucie
evolutionary-strategy,OneCube Evolve is a simple genetic algorithm library written in Java.
User: itssamuelrowe
evolutionary-strategy,ecr: Evolutionary Computation in R (version 2)
User: jakobbossek
Home Page: https://jakobbossek.github.io/ecr2/
evolutionary-strategy,Jenetics - Genetic Algorithm, Genetic Programming, Grammatical Evolution, Evolutionary Algorithm, and Multi-objective Optimization
User: jenetics
Home Page: https://jenetics.io
evolutionary-strategy,Blender/Bullet automatic parameter tuning/learning.
User: lettier
evolutionary-strategy,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
Organization: levinalab
evolutionary-strategy,A predator-prey model demonstrating an evolutionary arms race with respect to flocking behavior.
User: lukasmericle
evolutionary-strategy,The content of this repository will be inherent to the Computational Intelligence course at Polytechnic University of Turin academic year 2023/2024
User: marcocolangelo
evolutionary-strategy,An amateur attempt at breeding a chess-playing AI.
User: markzh
evolutionary-strategy,Tiny Genetic Algorithm in Python
User: moshesipper
evolutionary-strategy,Small experiments on MNIST to evaluate ES and GA against SGD
User: nihalsid
evolutionary-strategy,Paper: Challenges in High-dimensional Reinforcement Learning with Evolution Strategies
User: nimlr
evolutionary-strategy,Code for the paper "Evolved Policy Gradients"
Organization: openai
Home Page: https://arxiv.org/abs/1802.04821
evolutionary-strategy,Various studies show that criticality is an attractor in biological evolution. Which conditions have to be fulfilled, such that criticality acts as an attractor in our neuroevolution simulation? -- Masters Thesis Project ---
User: osrip
evolutionary-strategy,Paper: https://doi.org/10.1162/isal_a_00412 Which dynamical regime is beneficial for biological systems? Agent-based evolutionary foraging game with experiments to evaluate generalizability, ability to perform complex tasks and evolvability.
User: osrip
evolutionary-strategy,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
User: roaked
evolutionary-strategy,A evolutionary function in ruby which given a similarity score means the number of the similar bases and the dissimilar bases and then the sequence rate and the divergence rate calculates a ratio which can tell us how much sequencing depth to be covered
User: sablokgaurav
evolutionary-strategy,High performance implementation of Deep neuroevolution in pytorch using mpi4py. Intended for use on HPC clusters
User: sash-a
evolutionary-strategy,[WIP] Python implementation of evolution strategy based on Information Geometry. This library includes CMA-ES, NES, CompactGA and PBIL.
User: satuma777
evolutionary-strategy,Using Cartesian Genetic Programming to find an efficient Convolutional Neural Network architecture
User: scheckmedia
evolutionary-strategy,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.
User: sergio94al
Home Page: https://iopscience.iop.org/article/10.1088/2058-9565/ac1ab1
evolutionary-strategy,Esta aplicação fornece uma interface web a fim de demonstrar o uso do Algoritmo de colonização de formigas Antsystem
User: sergionoivak
evolutionary-strategy,A pure-Swift implementation of Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES).
User: sgonzalez
evolutionary-strategy,Application of Nonlinear Resource Utilization and Allocation Problem in Bicycle Resource Planning using Metaheuristic
User: shangchiwu
evolutionary-strategy,This github repository contains the official code for the paper, "Evolving Robust Neural Architectures to Defend from Adversarial Attacks"
User: shashankkotyan
Home Page: https://arxiv.org/abs/1906.11667
evolutionary-strategy,Multi Objective Evolutionary Algorithm for Soft Computing Applications
User: starrio
evolutionary-strategy,First assignment for Evolutionary Computing class at @vrije-universiteit-amsterdam
Organization: teameightyeight
evolutionary-strategy,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.
User: underwit
evolutionary-strategy,Experiments with Guided Evolutionary Strategies for Behavioral Robotics course project at Innopolis Univeristy
User: vkurenkov
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