Variable Metric Evolution Strategies for High-dimensional Multi-Objective Optimization
We design a class of variable metric evolution strategies well suited for high-dimensional problems. We target problems with many variables, not (necessarily) with many objectives. The construction combines two independent developments: efficient algorithms for scaling covariance matrix adaptation to high dimensions, and evolution strategies for multi-objective optimization. In order to design a specific instance of the class we first develop a (1+1) version of the limited memory matrix adaptation evolution strategy and then use an established standard construction to turn a population thereof into a state-of-the-art multi-objective optimizer with indicator-based selection. The method compares favorably to adaptation of the full covariance matrix.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Challenges in High-dimensional Reinforcement Learning with Evolution Strategies
Evolution Strategies (ESs) have recently become popular for training deep neural networks, in particular on reinforcement learning tasks, a special form of controller design. Compared to classic problems in continuous di…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Vocal Bursts Intensity PredictionResidual Learning Inspired Crossover Operator and Strategy Enhancements for Evolutionary Multitasking
In evolutionary multitasking, strategies such as crossover operators and skill factor assignment are critical for effective knowledge transfer. Existing improvements to crossover operators primarily focus on low-dimensio…
Super-ResolutionTransfer LearningAn Invariant Information Geometric Method for High-Dimensional Online Optimization
Sample efficiency is crucial in optimization, particularly in black-box scenarios characterized by expensive evaluations and zeroth-order feedback. When computing resources are plentiful, Bayesian optimization is often f…
Bayesian OptimizationMuJoCoNon-linear PCA via Evolution Strategies: a Novel Objective Function
Principal Component Analysis (PCA) is a powerful and popular dimensionality reduction technique. However, due to its linear nature, it often fails to capture the complex underlying structure of real-world data. While Ker…
Dimensionality ReductionGuided Evolutionary Strategies: Escaping the curse of dimensionality in random search
Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with, but not necessarily identical to, the …
Meta-LearningReinforcement Learning