Extreme compass and Dynamic Multi-Armed Bandits for Adaptive Operator Selection

The goal of adaptive operator selection is the on-line control of the choice of variation operators within evolutionary algorithms. The control process is based on two main components, the credit assignment, that defines the reward that will be used to evaluate the quality of an operator after it has been applied, and the operator selection mechanism, that selects one operator based on some operators qualities. Two previously developed adaptive operator selection methods are combined here: Compass evaluates the performance of operators by considering not only the fitness improvements from parent to offspring, but also the way they modify the diversity of the population, and their execution time; dynamic multi-armed bandit proposes a selection strategy based on the well-known UCB algorithm, achieving a compromise between exploitation and exploration, while nevertheless quickly adapting to changes. Tests with the proposed method, called ExCoDyMAB, are carried out using several hard instances of the satisfiability problem (SAT). Results show the good synergetic effect of combining both approaches

Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models

We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for time series regression models. In particular, we investigate the question of how to conduct finite sample inference on the parameters given an adaptive lasso model for some fixed value of the shrinkage parameter. Central in this study is the test of the hypothesis that a given adaptive lasso parameter equals zero, which therefore tests for a false positive. To this end we construct a simple testing procedure and show, theoretically and empirically through extensive Monte Carlo simulations, that the adaptive lasso combines efficient parameter estimation, variable selection, and valid finite sample inference in one step. Moreover, we analytically derive a bias correction factor that is able to significantly improve the empirical coverage of the test on the active variables. Finally, we apply the introduced testing procedure to investigate the relation between the short rate dynamics and the economy, thereby providing a statistical foundation (from a model choice perspective) to the classic Taylor rule monetary policy model

An Experimental Study of Adaptive Control for Evolutionary Algorithms

The balance of exploration versus exploitation (EvE) is a key issue on evolutionary computation. In this paper we will investigate how an adaptive controller aimed to perform Operator Selection can be used to dynamically manage the EvE balance required by the search, showing that the search strategies determined by this control paradigm lead to an improvement of solution quality found by the evolutionary algorithm

Adaptive BDDC in Three Dimensions

The adaptive BDDC method is extended to the selection of face constraints in three dimensions. A new implementation of the BDDC method is presented based on a global formulation without an explicit coarse problem, with massive parallelism provided by a multifrontal solver. Constraints are implemented by a projection and sparsity of the projected operator is preserved by a generalized change of variables. The effectiveness of the method is illustrated on several engineering problems.Comment: 28 pages, 9 figures, 9 table

Non stationary operator selection with island models

The purpose of adaptive operator selection is to choose dynamically the most suitable variation operator of an evolutionary algorithm at each iteration of the search process. These variation operators are applied on individuals of a population which evolves, according to an evolutionary process, in order to find an optimal solution. Of course the efficiency of an operator may change during the search and therefore its application should be precisely controlled. In this paper, we use dynamic island models as operator selection mechanisms. A sub-population is associated to each operators and individuals are allowed to migrate from one sub-population to another one. In order to evaluate the performance of this adaptive selection mechanism, we propose an abstract operator representation using fitness improvement distributions that allow us to define non stationary operators with mutual interactions. Our purpose is to show that the adaptive selection is able to identify not only good operators but also suitable sequences of operators

Hyperparameter Tuning in Bandit-Based Adaptive Operator Selection

EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, and EvoSTOC, Málaga, Spain, April 11-13, 2012, ProceedingsWe are using bandit-based adaptive operator selection while autotuning parallel computer programs. The autotuning, which uses evolutionary algorithm-based stochastic sampling, takes place over an extended duration and occurs in situ as programs execute. The environment or context during tuning is either largely static in one scenario or dynamic in another. We rely upon adaptive operator selection to dynamically generate worthy test configurations of the program. In this paper, we study how the choice of hyperparameters, which control the trade-off between exploration and exploitation, affects the effectiveness of adaptive operator selection which in turn affects the performance of the autotuner. We show that while the optimal assignment of hyperparameters varies greatly between different benchmarks, there exists a single assignment, for a context, of hyperparameters that performs well regardless of the program being tuned

Multi-UAV Coordination and Control Interface

Let’s imagine a fire control mission performed by multiple robots and commanded by a single operator. This scenario poses two challenges related to human factors: workload - the operator has to receive data, discover information, make decisions and send commands - and situational awareness - he/she has to know what is happening at any time of the mission. This work aims for the selection of the information that is shown by the interface to the operator. This information should be outlined according to the mission’s state and evolution together with the operator class, state and preferences. The expected result is an intelligent adaptive interface that provides the most relevant information for each task

A Comparison of Operator Utility Measures for On-Line Operator Selection in Local Search

This paper investigates the adaptive selection of operators in the context of Local Search. The utility of each operator is computed from the solution quality and distance of the candidate solution from the search trajectory. A number of utility measures based on the Pareto dominance relationship and the relative distances between the operators are proposed and evaluated on QAP instances using an implied or static target balance between exploitation and exploration. A refined algorithm with an adaptive target balance is then examined