In general Evolutionary Computation (EC) includes a number of optimization methods inspired by biological mechanisms of evolution. The methods catalogued in this area use the Darwinian principles of life evolution to produce algorithms that returns high quality solutions to hard-to-solve optimization problems. The main strength of EC is precisely that they provide good solutions even if the computational resources (e.g., running time) are limited. Astronomy and Astrophysics are two fields that often require optimizing problems of high complexity or analyzing a huge amount of data and the so-called complete optimization methods are inherently limited by the size of the problem/data. For instance, reliable analysis of large amounts of data is central to modern astrophysics and astronomical sciences in general. EC techniques perform well where other optimization methods are inherently limited (as complete methods applied to NP-hard problems), and in the last ten years, numerous proposals have come up that apply with greater or lesser success methodologies of evolutional computation to common engineering problems. Some of these problems, such as the estimation of non-lineal parameters, the development of automatic learning techniques, the implementation of control systems, or the resolution of multi-objective optimization problems, have had (and have) a special repercussion in the fields. For these reasons EC emerges as a feasible alternative for traditional methods. In this paper, we discuss some promising applications in this direction and a number of recent works in this area; the paper also includes a general description of EC to provide a global perspective to the reader and gives some guidelines of application of EC techniques for future research

In this correspondence, we obtain exact recovery conditions for regularized modified basis pursuit (reg-mod-BP) and discuss when the obtained conditions are weaker than those for modified-CS or for basis pursuit (BP). The discussion is also supported by simulation comparisons. Reg-mod-BP provides a solution to the sparse recovery problem when both an erroneous estimate of the signal's support, denoted by $T$, and an erroneous estimate of the signal values on $T$ are available.

Nearly 20% of total energy consumption in the United States is accounted for in heating, ventilation, and air conditioning (HVAC) systems. Smart sensing and adaptive energy management agents can greatly decrease the energy usage of HVAC systems in many building applications, for example by enabling the operator to shut off HVAC to unoccupied rooms. We implement a multimodal sensor agent that is nonintrusive and low-cost, combining information such as motion detection, CO2 reading, sound level, ambient light,and door state sensing. We show that in our live test bed at the USC campus, these sensor agents can be used to accurately estimate the number of occupants in each room using machine learning techniques, and that these techniques can also be applied to predict future occupancy by creating agent models of the occupants. These predictions will be used by control agents to enable the HVAC system increase its efficiency by continuously adapting to occupancy forecasts of each room.

This paper presents work using crowdsourcing to assess explanations for supervised text classification. In this paper, an explanation is defined to be a set of words from the input text that a classifier or human believes to be most useful for making a classification decision. We compared two types of explanations for classification decisions: human-generated and computer-generated. The comparison is based on whether the type of the explanation was identifiable and on which type of explanation was preferred. Crowdsourcing was used to collect two types of data for these experiments. First, human-generated explanations were collected by having users select an appropriate category for a piece of text and highlight words that best support this category. Second, users were asked to compare human- and computer-generated explanations and indicate which they preferred and why. The crowdsourced data used for this paper was collected primarily via Amazon’s Mechanical Turk, using several quality control methods. We found that in one test corpus, the two explanation types were virtually indistinguishable, and that participants did not have a significant preference for one type over another. For another corpus, the explanations were slightly more distinguishable, and participants preferred the computer-generated explanations at a small, but statistically significant, level. We conclude that computer-generated explanations for text classification can be comparable in quality to human-generated explanations.

Natural world interaction (NWI), the pursuit of arbitrary goals in unstructured physical environments, is an excellent motivating problem for the reintegration of artificial intelligence. It is the problem set that humans struggle to solve. At a minimum it entails perception, learning, planning, and control, and can also involve language and social behavior. An agent's fitness in NWI is achieved by being able to perform a wide variety of tasks, rather than being able to excel at one. In an attempt to address NWI, a brain-emulating cognition and control architecture (BECCA) was developed. It uses a combination of feature creation and model-based reinforcement learning to capture structure in the environment in order to maximize reward. BECCA avoids making common assumptions about its world, such as stationarity, determinism, and the Markov assumption. BECCA has been demonstrated performing a set of tasks which is non-trivially broad, including a vision-based robotics task. Current development activity is focused on applying BECCA to the problem of general Search and Retrieve, a representative natural world interaction task.

The starting point of this article is the question "How to retrieve fingerprints of rhythm in written texts?" We address this problem in the case of Brazilian and European Portuguese. These two dialects of Modern Portuguese share the same lexicon and most of the sentences they produce are superficially identical. Yet they are conjectured, on linguistic grounds, to implement different rhythms. We show that this linguistic question can be formulated as a problem of model selection in the class of variable length Markov chains. To carry on this approach, we compare texts from European and Brazilian Portuguese. These texts are previously encoded according to some basic rhythmic features of the sentences which can be automatically retrieved. This is an entirely new approach from the linguistic point of view. Our statistical contribution is the introduction of the smallest maximizer criterion which is a constant free procedure for model selection. As a by-product, this provides a solution for the problem of optimal choice of the penalty constant when using the BIC to select a variable length Markov chain. Besides proving the consistency of the smallest maximizer criterion when the sample size diverges, we also make a simulation study comparing our approach with both the standard BIC selection and the Peres-Shields order estimation. Applied to the linguistic sample constituted for our case study, the smallest maximizer criterion assigns different context-tree models to the two dialects of Portuguese. The features of the selected models are compatible with current conjectures discussed in the linguistic literature.

Graphical models use graphs to compactly capture stochastic dependencies amongst a collection of random variables. Inference over graphical models corresponds to finding marginal probability distributions given joint probability distributions. In general, this is computationally intractable, which has led to a quest for finding efficient approximate inference algorithms. We propose a framework for generalized inference over graphical models that can be used as a wrapper for improving the estimates of approximate inference algorithms. Instead of applying an inference algorithm to the original graph, we apply the inference algorithm to a block-graph, defined as a graph in which the nodes are non-overlapping clusters of nodes from the original graph. This results in marginal estimates of a cluster of nodes, which we further marginalize to get the marginal estimates of each node. Our proposed block-graph construction algorithm is simple, efficient, and motivated by the observation that approximate inference is more accurate on graphs with longer cycles. We present extensive numerical simulations that illustrate our block-graph framework with a variety of inference algorithms (e.g., those in the libDAI software package). These simulations show the improvements provided by our framework.

It is known that fixed points of loopy belief propagation (BP) correspond to stationary points of the Bethe variational problem, where we minimize the Bethe free energy subject to normalization and marginalization constraints. Unfortunately, this does not entirely explain BP because BP is a dual rather than primal algorithm to solve the Bethe variational problem -- beliefs are infeasible before convergence. Thus, we have no better understanding of BP than as an algorithm to seek for a common zero of a system of non-linear functions, not explicitly related to each other. In this theoretical paper, we show that these functions are in fact explicitly related -- they are the partial derivatives of a single function of reparameterizations. That means, BP seeks for a stationary point of a single function, without any constraints. This function has a very natural form: it is a linear combination of local log-partition functions, exactly as the Bethe entropy is the same linear combination of local entropies.

Abstract: A new probabilistic methodology for transmission expansion planning (TEP) th at does not require a priori specification of new/additional transmission capacities and uses the concept of social welfare has been proposed. Two new concepts have been introduced in this paper: (i) roulette wheel methodology has been used to calculate t he capacity of new transmission lines and (ii) load flow analysis has been used to calculate expected demand not served (EDNS). The overall methodology has been implemented on a modified IEEE 5 - bus test system. Simulations show an important result: addit ion of only new transmission lines is not sufficient to minimize EDNS. Nowadays, the need for appropriate planned power syste ms to reduce generation cost, minimize the consumer cost and improve the quality of the power supply has become imperative [1] - [3]. As a result, transmission expansion planning (TEP) is gaining more significance.

Sparsity-promoting priors have become increasingly popular over recent years due to an increased number of regression and classification applications involving a large number of predictors. In time series applications where observations are collected over time, it is often unrealistic to assume that the underlying sparsity pattern is fixed. We propose here an original class of flexible Bayesian linear models for dynamic sparsity modelling. The proposed class of models expands upon the existing Bayesian literature on sparse regression using generalized multivariate hyperbolic distributions. The properties of the models are explored through both analytic results and simulation studies. We demonstrate the model on a financial application where it is shown that it accurately represents the patterns seen in the analysis of stock and derivative data, and is able to detect major events by filtering an artificial portfolio of assets.