Versatile Black-Box Optimization
Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization. It is also a good tool for making evolutionary algorithms fast, robust and versatile. We present Shiwa, an algorithm good at both discrete and continuous, noisy and noise-free, sequential and parallel, black-box optimization. Our algorithm is experimentally compared to competitors on YABBOB, a BBOB comparable testbed, and on some variants of it, and then validated on several real world testbeds.
Code (0)
등록된 구현이 없습니다.
Tasks
Combinatorial OptimizationEvolutionary AlgorithmsSimilar Papers 제목 키워드 기반
Multi-Strategy Coevolving Aging Particle Optimization
We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In t…
Tree-Structured Parzen Estimator Can Solve Black-Box Combinatorial Optimization More Efficiently
Tree-structured Parzen estimator (TPE) is a versatile hyperparameter optimization (HPO) method supported by popular HPO tools. Since these HPO tools have been developed in line with the trend of deep learning (DL), the p…
Hyperparameter OptimizationQDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization purposes, ranging from black-box optimi…
continuous-controlContinuous ControlDiversityreinforcement-learning+1TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments
In explainable artificial intelligence, there is increasing interest in understanding the behaviour of autonomous agents to build trust and validate performance. Modern agent architectures, such as those trained by deep …
Deep Reinforcement LearningExplainable artificial intelligencereinforcement-learningReinforcement Learning (RL)Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on substitute models and achieves state-of-the-…
Malware Detection