Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations
arXiv:2009.05700
Abstract
Many real-world applications involve black-box optimization of multiple objectives using continuous function approximations that trade-off accuracy and resource cost of evaluation. For example, in rocket launching research, we need to find designs that trade-off return-time and angular distance using continuous-fidelity simulators (e.g., varying tolerance parameter to trade-off simulation time and accuracy) for design evaluations. The goal is to approximate the optimal Pareto set by minimizing the cost for evaluations. In this paper, we propose a novel approach referred to as information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations (iMOCA)} to solve this problem. The key idea is to select the sequence of input and function approximations for multiple objectives which maximize the information gain per unit cost for the optimal Pareto front. Our experiments on diverse synthetic and real-world benchmarks show that iMOCA significantly improves over existing single-fidelity methods.
References in corpus (11)
- Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
- Max-value Entropy Search for Efficient Bayesian Optimization
- Predictive Entropy Search for Multi-objective Bayesian Optimization
- Multi-fidelity Bayesian Optimisation with Continuous Approximations
- Bayesian Optimization of Combinatorial Structures
- A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes
- Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its parallelization
- Practical Bayesian Optimization for Variable Cost Objectives
- Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints
- Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints
- Scalable Combinatorial Bayesian Optimization with Tractable Statistical models