Sakura M's repositories
apollo
An open autonomous driving platform
aspiration-study
Artifact for the submitted paper
bnn-bo
Bayesian Neural Network Surrogates for Bayesian Optimization
DEAOE_Code
Codes for the algorithm DEAOE proposed in the paper Distributed and Expensive Evolutionary Constrained Optimization with On-Demand Evaluation
DirHV-EGO
Hypervolume-Guided Decomposition for Parallel Expensive Multiobjective Optimization
eToSADE_Code
Codes for the algorithm eToSADE proposed in the paper An Efficient Two-Stage Surrogate-Assisted Differential Evolution for Expensive Inequality Constrained Optimization
Flash-MultiConfig
Multi-objective performance optimization for highly configurable systems
HVCTR
Hypervolume-Based Cooperative Coevolution with Two Reference Points for Multi-Objective Optimization
jMetal
jMetal: a framework for multi-objective optimization with metaheuristics
jMetalPy
A framework for single/multi-objective optimization with metaheuristics
jMetalSP
A framework for Big Data Optimization with multi-objective metaheuristics
PlatEMO
Evolutionary multi-objective optimization platform
QDax_Individual_Edition
Accelerated Quality-Diversity
HRCEA_Code
Codes for the algorithm HRCEA proposed in the paper A Hybrid Regressor and Classifier-Assisted Evolutionary Algorithm for Expensive Optimization with Incomplete Constraint Information
HSMEA
Hybrid Surrogate Assisted Many-objective Evolutionary Algorithm for Expensive Multi/Many Objective Optimization Problems
laplace-bayesopt
Laplace approximated BNN surrogate for BoTorch
MAP-Elites
Python implementation of the genetic algorithm MAP-Elites with applications in constrained optimization
POAP
Plumbing for Optimization with Asynchronous Parallelism
pymop
Single- as well as Multi-Objective Optimization Test Problems: ZDT, DTLZ, CDTLZ, CTP, BNH, OSY, ...
pySOT
Surrogate Optimization Toolbox for Python
REMO
Expensive Multiobjective Optimization by Relation Learning and Prediction
RL_TSP_4static
Deep Reinforcement Learning for Multiobjective Optimization. Code for this paper