The ability to identify sentiment in text, referred to as sentiment analysis, is one which is natural to adult humans. This task is, however, not one which a computer can perform by default. Identifying sentiments in an automated, algorithmic manner will be a useful capability for business and research in their search to understand what consumers think about their products or services and to understand human sociology. Here we propose two new Genetic Algorithms (GAs) for the task of automated text sentiment analysis. The GAs learn whether words occurring in a text corpus are either sentiment or amplifier words, and their corresponding magnitude. Sentiment words, such as 'horrible', add linearly to the final sentiment. Amplifier words in contrast, which are typically adjectives/adverbs like 'very', multiply the sentiment of the following word. This increases, decreases or negates the sentiment of the following word. The sentiment of the full text is then the sum of these terms. This approach grows both a sentiment and amplifier dictionary which can be reused for other purposes and fed into other machine learning algorithms. We report the results of multiple experiments conducted on large Amazon data sets. The results reveal that our proposed approach was able to outperform several public and/or commercial sentiment analysis algorithms.

In this article, I am going to explain the concept of genetic algorithm. First, I am going to present its origin and its goal. Then I am going to show you how to implement a genetic algorithm with a short python tutorial. The naive solution is to create an "empirical algorithm" which is a set of rules: "if you meet this conditions, act like that". I could imagine that with enough rules like this we could reproduce natural intelligence.

Depending on whether you embrace or fear the robo-future of design, Mark Maker (via Sidebar) could be considered either the beginning of the end, or proof that such fears are overstated, because bots are still pretty crap at design. The system then uses a genetic algorithm–a kind of program that mimics natural selection–to generate an endless succession of logos. When you like a logo, you click a heart, which tells the system to generate more logos like it. By liking enough logos, the idea is that Mark Maker can eventually generate one that suits your needs, without ever employing a human designer. Mark Maker creates its logos by breaking each design in half, so that it contains both a base design and an accent element.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The network models in our study were from existing references. All the underlying data set for our study could be available from the eleven sources listed below. Superfamilies of designed and evolved networks. Funding: This research is supported by the National Science Foundation of China with granting No.61773032.

The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored. We examine how natural gradients can be used in non-conjugate stochastic settings, together with hyperparameter learning. We conclude that the natural gradient can significantly improve performance in terms of wall-clock time. For ill-conditioned posteriors the benefit of the natural gradient method is especially pronounced, and we demonstrate a practical setting where ordinary gradients are unusable. We show how natural gradients can be computed efficiently and automatically in any parameterization, using automatic differentiation. Our code is integrated into the GPflow package.

NetMillennium, Inc. has recently donated GA Grid to Apache Ignite's Machine Learning (ML) module. GA Grid is a distributive Genetic Algorithm (GA) used to solve complex problems by simulating biological evolution. GA's are a form of Machine Learning (ML), excellent for finding an optimal solution, among possibly thousands (or more) candidate solutions for a given domain. Real world uses of GA's include: automotive design, computer gaming, robotics, investments, traffic/shipment routing just to name a few. In GA Grid, all genetic operations: Fitness Calculation, Crossover, and Mutation are modeled as a ComputeTask for distributive behavior.

Rather than being fearful of machines rising up against humans, one company is actively trying to merge the two, by combining human intelligence with computer algorithms to predict a whole series of real-world events. Unanimous AI is a company that uses technology that draws from a concept commonly found in nature: swarm intelligence. Rather than using algorithms to replace human intelligence, the firm tries to amplify it. "The artificial swarm intelligence really refers to the way in which we actually combine humans with technology in order to come to these amplified outsets, or amplified outcomes," David Baltaxe, chief intelligence officer at Unanimous AI, said on Tuesday. Biologists and zoologists have been studying swarm intelligence in systems of insects and animals, like fishes, birds and honeybees, for a long period of time, Baltaxe told CNBC at the Credit Suisse Asian Investment Conference.

In this paper, we consider a realistic and meaningful scenario in the context of smart grids where an electricity retailer serves three different types of customers, i.e., customers with an optimal home energy management system embedded in their smart meters (C-HEMS), customers with only smart meters (C-SM), and customers without smart meters (C-NONE). The main objective of this paper is to support the retailer to make optimal day-ahead dynamic pricing decisions in such a mixed customer pool. To this end, we propose a two-level decision-making framework where the retailer acting as upper-level agent firstly announces its electricity prices of next 24 hours and customers acting as lower-level agents subsequently schedule their energy usages accordingly. For the upper level problem, we optimize the dynamic prices for the retailer to maximize its profit subject to realistic market constraints. The above two-level model is tackled by genetic algorithms (GA) based distributed optimization methods while its feasibility and effectiveness are con-2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. Please cite this accepted article as: Fanlin Meng, Xiao-Jun Zeng, Yan Zhang, Chris J. Dent, Dunwei Gong, An Integrated Optimization Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids, Information Sciences (2018), doi: 10.1016/j.ins.2018.03.039 Preprint submitted to Information Sciences March 22, 2018 firmed via simulation results. Keywords: Bilevel Modelling, Genetic Algorithms, Machine Learning, Dynamic Pricing, Demand-side Management, Demand Response, Smart Grids 1. Introduction With the large-scale deployment of smart meters and two-way communication infrastructures, dynamic pricing based demand response and demand-side management programs [37] [12] have attracted enormous attentions from both academia and industry and are expected to bring great benefits to the whole power system. Real-time pricing (RTP), timeof-use pricing (ToU) and critical-peak pricing (CPP) are commonly used dynamic pricing strategies [20].

Recently, feature selection has become an increasingly important area of research due to the surge in high-dimensional datasets in all areas of modern life. A plethora of feature selection algorithms have been proposed, but it is difficult to truly analyse the quality of a given algorithm. Ideally, an algorithm would be evaluated by measuring how well it removes known bad features. Acquiring datasets with such features is inherently difficult, and so a common technique is to add synthetic bad features to an existing dataset. While adding noisy features is an easy task, it is very difficult to automatically add complex, redundant features. This work proposes one of the first approaches to generating redundant features, using a novel genetic programming approach. Initial experiments show that our proposed method can automatically create difficult, redundant features which have the potential to be used for creating high-quality feature selection benchmark datasets. Keywords: Genetic Programming, Feature Creation, Feature Construction, Feature Selection, Mutual Information, Evolutionary Computation 1 Introduction Feature Selection (FS) techniques aim to remove features from a dataset which are less useful than others.

Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization control in dynamical systems as an optimization problem and present a multi-objective genetic programming-based approach to infer optimal control functions that drive the system from a synchronized to a non-synchronized state and vice-versa. The genetic programming-based controller allows learning optimal control functions in an interpretable symbolic form. The effectiveness of the proposed approach is demonstrated in controlling synchronization in coupled oscillator systems linked in networks of increasing order complexity, ranging from a simple coupled oscillator system to a hierarchical network of coupled oscillators. The results show that the proposed method can learn highly-effective and interpretable control functions for such systems.