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.

These combinations of real-time biological systems can blend knowledge, exploration, and exploitation to unify intelligence and solve problems more efficiently. These simple agents interact locally, within their environment, and new behaviors emerge from the group as a whole. In the world of evolutionary alogirthms one such inspired method is particle swarm optimization (PSO). It is a swarm intelligence based computational technique that can be used to find an approximate solution to a problem by iteratively trying to search candidate solutions (called particles) with regard to a given measure of quality around a global optimum. The movements of the particles are guided by their own best known position in the search-space as well as the entire swarm's best known position.

Artificial intelligence might arguably be the newest frontier of human experience, but there's no denying that man has been fascinated with the concept for millennia. From the mythical stories of Hephaestus creating mechanical servants and brazen-footed bulls that puffed fire from their mouths, to the talking heads of the 13th century, to IBM Watson and modern forms of AI, the subject has been bubbling on the surface of human consciousness. The time is now here for AI to come of age; and, in many ways, it already has. But now there's a new problem, and it's not one of how AI can be implemented, as has been the major challenge in the past. AI has now sprouted into a plethora of forms, each rivaling the other in an attempt to showcase its superior capabilities.

Did you know that there's a way to use the power of natural selection to solve programming challenges? It's when you want to find not just a valid solution but the solution that will give you the best results. For example, if you have a backpack that only fits a certain amount of stuff and you want to maximize the amount of stuff you can bring, then you could use a genetic algorithm to find the best solution. This is also known as *the knapsack problem*. The genetic algorithm is not the only way to solve this kind of problem, but it's an interesting one because it's modeled after real-world behavior.