scholarly journals Bayesian Optimization in a Billion Dimensions via Random Embeddings

2016 ◽  
Vol 55 ◽  
pp. 361-387 ◽  
Author(s):  
Ziyu Wang ◽  
Frank Hutter ◽  
Masrour Zoghi ◽  
David Matheson ◽  
Nando De Feitas
Keyword(s):  
User Interfaces ◽  
Optimization Techniques ◽  
Bayesian Optimization ◽  
Mixed Integer ◽  
Continuous Variables ◽  
High Dimensions ◽  
Intelligent User Interfaces ◽  
Automatic Algorithm Configuration ◽  
Algorithm Configuration ◽  
Random Embeddings

Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several workshops on Bayesian optimization have identified its scaling to high-dimensions as one of the holy grails of the field. In this paper, we introduce a novel random embedding idea to attack this problem. The resulting Random EMbedding Bayesian Optimization (REMBO) algorithm is very simple, has important invariance properties, and applies to domains with both categorical and continuous variables. We present a thorough theoretical analysis of REMBO. Empirical results confirm that REMBO can effectively solve problems with billions of dimensions, provided the intrinsic dimensionality is low. They also show that REMBO achieves state-of-the-art performance in optimizing the 47 discrete parameters of a popular mixed integer linear programming solver.

scholarly journals ParamILS: An Automatic Algorithm Configuration Framework

2009 ◽  
Vol 36 ◽  
pp. 267-306 ◽  
Author(s):  
F. Hutter ◽  
H. H. Hoos ◽  
K. Leyton-Brown ◽  
T. Stuetzle
Keyword(s):  
Mixed Integer ◽  
Considerable Effort ◽  
Performance Improvements ◽  
Automatic Algorithm Configuration ◽  
Automated Algorithm ◽  
Algorithm Configuration ◽  
Consistent Performance ◽  
Problem Instances ◽  
Automatic Algorithm ◽  
Configuration Problem

The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm’s performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements.

scholarly journals Unbalanced Budget Distribution for Automatic Algorithm Configuration

2021 ◽  
Author(s):  
Soheila Ghambari ◽  
Hojjat Rakhshani ◽  
Julien Lepagnot ◽  
Laetitia Jourdan ◽  
Lhassane Idoumghar
Keyword(s):  
Random Search ◽  
Time Budget ◽  
Bayesian Optimization ◽  
Test Problems ◽  
Nsga Ii ◽  
Automatic Algorithm Configuration ◽  
Empirical Performance ◽  
Algorithm Configuration ◽  
Problem Instances ◽  
Automatic Algorithm

Abstract Optimization algorithms often have several critical setting parameters and the improvement of the empirical performance of these algorithms depends on tuning them. Manually configuration of such parameters is a tedious task that results in unsatisfactory outputs. Therefore, several automatic algorithm configuration frameworks have been proposed to regulate the parameters of a given algorithm for a series of problem instances. Although the developed frameworks perform very well to deal with various problems, however, there is still a trade-off between the accuracy and budget requirements that need to be addressed. This work investigates the performance of unbalanced distribution of budget for different configurations to deal with the automatic algorithm configuration problem. Inspired by the bandit-based approaches, the main goal is to find a better configuration that substantially improves the performance of the target algorithm while using a smaller run time budget. In this work, non-dominated sorting genetic algorithm II (NSGA-II) is employed as a target algorithm using jMetalPy software platform and the multimodal multi-objective optimization (MMO) test suite of CEC'2020 is used as a set of test problems. We did a comprehensive comparison with other known methods including random search, Bayesian optimization, SMAC, ParamILS, irace, and MAC methods. The experimental results interestingly proved the efficiency of the proposed approach for automatic algorithm configuration with a minimum time budget in comparison with other competitors.

Investigating a Virtual Reality-based Emergency Response Scenario and Intelligent User Interface for First Responders

2020 ◽  
Vol 64 (1) ◽  
pp. 2114-2118
Author(s):  
Randall Spain ◽  
Jason Saville ◽  
Barry Lui ◽  
Donia Slack ◽  
Edward Hill ◽  
...  
Keyword(s):  
Virtual Reality ◽  
User Interface ◽  
Emergency Response ◽  
User Interfaces ◽  
Mental Workload ◽  
First Responders ◽  
Lessons Learned ◽  
Future Research ◽  
Intelligent User Interfaces ◽  
Intelligent User Interface

Because advances in broadband capabilities will soon allow first responders to access and use many forms of data when responding to emergencies, it is becoming critically important to design heads-up displays to present first responders with information in a manner that does not induce extraneous mental workload or cause undue interaction errors. Virtual reality offers a unique medium for envisioning and testing user interface concepts in a realistic and controlled environment. In this paper, we describe a virtual reality-based emergency response scenario that was designed to support user experience research for evaluating the efficacy of intelligent user interfaces for firefighters. We describe the results of a usability test that captured firefighters’ feedback and reactions to the VR scenario and the prototype intelligent user interface that presented them with task critical information through the VR headset. The paper concludes with lessons learned from our development process and a discussion of plans for future research.

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1392 ◽  
Author(s):  
Iram Parvez ◽  
JianJian Shen ◽  
Mehran Khan ◽  
Chuntian Cheng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Stochastic Programming ◽  
Short Interval ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Fuzzy Logics ◽  
Spinning Reserve ◽  
Lagrange Relaxation ◽  
Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

When actions have consequences: empirically based decision making for intelligent user interfaces

2001 ◽  
Vol 14 (1-2) ◽  
pp. 75-92 ◽  
Author(s):  
A Jameson ◽  
B Großmann-Hutter ◽  
L March ◽  
R Rummer ◽  
T Bohnenberger ◽  
...  
Keyword(s):  
Decision Making ◽  
User Interfaces ◽  
Intelligent User Interfaces

An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and its Applications to Mechanical Design

1993 ◽  
Author(s):  
B. K. Kannan ◽  
Steven N. Kramer
Keyword(s):  
Lagrange Multiplier ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Continuous Optimization ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Augmented Lagrange Multiplier

Abstract An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one and continuous variables is presented. The augmented Lagrange multiplier method combined with Powell’s method and Fletcher & Reeves Conjugate Gradient method are used to solve the optimization problem where penalties are imposed on the constraints for integer / discrete violations. The use of zero-one variables as a tool for conceptual design optimization is also described with an example. Several case studies have been presented to illustrate the practical use of this algorithm. The results obtained are compared with those obtained by the Branch and Bound algorithm. Also, a comparison is made between the use of Powell’s method (zeroth order) and the Conjugate Gradient method (first order) in the solution of these mixed variable optimization problems.

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