Java (optimization) and Python (hypertuning, plots) code with implementation of many global random optimization methods written as part of my MSc in Data Science degree.
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Updated
Dec 30, 2021 - Java
Java (optimization) and Python (hypertuning, plots) code with implementation of many global random optimization methods written as part of my MSc in Data Science degree.
Optimziation includes a class of methods to find global or local optimums for discrete or continuous objectives; from evolutionary-based algorithms to swarm-based ones.
This collection of algorithms is part of a lecture on computational intelligence at ZHAW.
Weight optimization of a truss structure via evolution strategy.
(GECCO 2023) Natural Evolution Strategy for Mixed-Integer Black-Box Optimization
Gentech, A Genetic Algorithm Solver
Evolution Strategy Histogram Equalization
Design of a surrogate-assisted (mu/mu,lambda)-ES
My Evolutionary Computing Lecture Project Codes Relies Here In Peace.
(EvoApps2022) "Towards a Principled Learning Rate Adaptation for Natural Evolution Strategies"
(GECCO2023 Best Paper Nomination) CMA-ES with Learning Rate Adaptation
CMA-ES in MATLAB
(CEC2022) Fast Moving Natural Evolution Strategy for High-Dimensional Problems
Minimal PyTorch Library for Natural Evolution Strategies
Unity In Editor Deep Learning Tools. Using KerasSharp, TensorflowSharp, Unity MLAgent. In-Editor training and no python needed.
Flappy Bird AI using Evolution Strategies
Python library for CMA Evolution Strategy.
Evolutionary Algorithm using Python, 莫烦Python 中文AI教学
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