The cooperative group optimization (CGO) system consists of a group of intelligent agents cooperating with their peers in a sharing environment for realizing a common intention of finding high-quality solution(s) based on the landscape representation of an optimization task. CGO has also been applied on numerical optimization problem (NOP) to find solutions in high-dimensional nonlinear continuous space. Some algorithms, including Dissipative Particle Swarm Optimization (DPSO), Differential Evolution (DE), Social Cognitive Optimization (SCO), Genetic Algorithms (GA), and Electromagnetism-like Mechanism (EM) Heuristic, etc, and their hybrids (e.g., DEPSO), could be easily implemented into CGO. Both SCO and DEPSO have been incorporated into the NLPSolver extension of Calc in Apache Office. DEPSO was used for finding narrow admissible k-tuples.

Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.

Context: Topic modeling finds human-readable structures in unstructured textual data. A widely used topic modeler is Latent Dirichlet allocation. When run on different datasets, LDA suffers from "order effects" i.e. different topics are generated if the order of training data is shuffled. Such order effects introduce a systematic error for any study. This error can relate to misleading results;specifically, inaccurate topic descriptions and a reduction in the efficacy of text mining classification results. Objective: To provide a method in which distributions generated by LDA are more stable and can be used for further analysis. Method: We use LDADE, a search-based software engineering tool that tunes LDA's parameters using DE (Differential Evolution). LDADE is evaluated on data from a programmer information exchange site (Stackoverflow), title and abstract text of thousands ofSoftware Engineering (SE) papers, and software defect reports from NASA. Results were collected across different implementations of LDA (Python+Scikit-Learn, Scala+Spark); across different platforms (Linux, Macintosh) and for different kinds of LDAs (VEM,or using Gibbs sampling). Results were scored via topic stability and text mining classification accuracy. Results: In all treatments: (i) standard LDA exhibits very large topic instability; (ii) LDADE's tunings dramatically reduce cluster instability; (iii) LDADE also leads to improved performances for supervised as well as unsupervised learning. Conclusion: Due to topic instability, using standard LDA with its "off-the-shelf" settings should now be depreciated. Also, in future, we should require SE papers that use LDA to test and (if needed) mitigate LDA topic instability. Finally, LDADE is a candidate technology for effectively and efficiently reducing that instability.

Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successfully applied to solving expensive black-box problems in engineering and machine learning. Here we explore whether BO can be applied as a general tool for model fitting. First, we present a novel hybrid BO algorithm, Bayesian adaptive direct search (BADS), that achieves competitive performance with an affordable computational overhead for the running time of typical models. We then perform an extensive benchmark of BADS vs. many common and state-of-the-art nonconvex, derivative-free optimizers, on a set of model-fitting problems with real data and models from six studies in behavioral, cognitive, and computational neuroscience. With default settings, BADS consistently finds comparable or better solutions than other methods, including `vanilla' BO, showing great promise for advanced BO techniques, and BADS in particular, as a general model-fitting tool.

We study the notion of robustness in stable matching problems. We first define robustness by introducing (a,b)-supermatches. An (a, b)-supermatch is a stable matching in which if any a pairs break up it is possible to find another stable matching by changing the partners of those a pairs and the partners of at most b other pairs. In this context, we define the most robust stable matching as a (1, b)- supermatch where b is minimum. We first show that checking whether a given stable matching is a (1, b)-supermatch can be done in polynomial time. Next, we use this procedure to design a constraint programming model, a local search approach, and a genetic algorithm to find the most robust stable matching. Our empirical evaluation on large instances shows that local search outperforms the other approaches.

Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectively exploited by those models. In recent years, several works have been using complex networks for data representation and analysis. However, no feature learning method has been proposed for such category of techniques. Here, we present an unsupervised feature learning mechanism that works on datasets with binary features. First, the dataset is mapped into a feature--sample network. Then, a multi-objective optimization process selects a set of new vertices to produce an enhanced version of the network. The new features depend on a nonlinear function of a combination of preexisting features. Effectively, the process projects the input data into a higher-dimensional space. To solve the optimization problem, we design two metaheuristics based on the lexicographic genetic algorithm and the improved strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced network contains more information and can be exploited to improve the performance of machine learning methods. The advantages and disadvantages of each optimization strategy are discussed.

In a world filled with natural disasters, war, and growing risk of incurable infections, one study is shining a little ray of hope that perhaps the future won't be as grim as we imagine. The study found subtle evidence to suggest that certain genetic diseases, such as Alzheimer's and asthma, may be weeded out by natural selection. Sure, it may take a few thousand years, but at least our future generations would have something to look forward to. Natural selection is nature's way to ensure life keeps on living despite drastic changes in environment and lifestyle. You may think that humans have reached their evolutionary peak, but the research found evidence that genetic variants that influence fertility in the U.S. and UK are slowly changing, and may be working to weed out certain mutations that lead to a number of serious health conditions, The Independent reported.

Many studies have found evidence of rapid evolution in response to environmental change. In most cases, there has been some suggestion of which traits might be most responsive ahead of time. Bosse et al. turn this approach on its head by using genomic regions with a signature of selection to identify traits that are changing. In great tits (Parus major) in the United Kingdom, genomic regions showing selection invariably contained genes associated with bill growth. Indeed, U.K. birds not only have longer bills, but these longer bills are associated with increased fitness.

This chapter discusses the possibility of instilling a virtual world with mechanisms for evolution and natural selection in order to generate rich ecosystems of complex organisms in a process akin to biological evolution. Some previous work in the area is described, and successes and failures are discussed. The components of a more comprehensive framework for designing such worlds are mapped out, including the design of the individual organisms, the properties and dynamics of the environmental medium in which they are evolving, and the representational relationship between organism and environment. Some of the key issues discussed include how to allow organisms to evolve new structures and functions with few restrictions, and how to create an interconnectedness between organisms in order to generate drives for continuing evolutionary activity.

On Sunday night, a lone gunman armed with 23 powerful weapons opened fire from a window on the 32nd floor of the Mandalay Bay casino in Las Vegas, targeting thousands concertgoers gathered for a country music festival below. Within minutes, the gunman had killed at least 58 people and injured nearly 500. There may be no more contentious issue in America than gun control, and no more emotional time to discuss it than in the days following a national tragedy like the one that unfolded in Las Vegas. But, a subject being difficult to discuss should not preclude us from trying to understand it, and fortunately nature has evolved methods for helping relatively simple organisms work through incredibly complicated problems with life or death consequences. Swarm Intelligence allows groups of bees to converge on the perfect place for their hive nearly 90% of the time, and extending this power to humans through Unanimous AI's Swarm AI technology empowers groups to create similarly optimized insight.