Neural Information Processing Systems http://nips.cc/

We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto-set of solutions by minimizing the number of function evaluations. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive simulations. We propose a novel approach referred to as Max-value Entropy Search for Multi-objective Optimization (MESMO) to solve this problem. MESMO employs an output-space entropy based acquisition function to efficiently select the sequence of inputs for evaluation for quickly uncovering high-quality solutions. We also provide theoretical analysis to characterize the efficacy of MESMO. Our experiments on several synthetic and real-world benchmark problems show that MESMO consistently outperforms state-of-the-art algorithms.

For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive computational simulations.

However, please note that we clearly mentioned that this work is an extension of MES to MO setting.

We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto-set of solutions by minimizing the number of function evaluations. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive simulations. We propose a novel approach referred to as Max-value Entropy Search for Multi-objective Optimization (MESMO) to solve this problem. MESMO employs an output-space entropy based acquisition function to efficiently select the sequence of inputs for evaluation for quickly uncovering high-quality solutions. We also provide theoretical analysis to characterize the efficacy of MESMO.

Universidad Tecnológica del Uruguay, Rivera, Uruguay 2 ABSTRACT The implementation of robots to enhance some processes has become popular in recent years due to the accelerated way of production in some factories. Within this context was where robotics has emerged, firstly with stationary robots and more recently mobile robots, namely aerial and terrestrial robots. They can be used for delimited processes within a function, mainly the stationary robots, but also for research in wider areas and even competition. This work summarizes the construction of a model of terrestrial mobile robot that makes the use of artificial intelligence for the purpose of research and competitions, all of that with the basic sensing that can be used in industry. (Rovas, 2015).

We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto-set of solutions by minimizing the number of function evaluations. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive simulations. We propose a novel approach referred to as Max-value Entropy Search for Multi-objective Optimization (MESMO) to solve this problem. MESMO employs an output-space entropy based acquisition function to efficiently select the sequence of inputs for evaluation for quickly uncovering high-quality solutions. We also provide theoretical analysis to characterize the efficacy of MESMO.