The choice of a proper learning rate is paramount for good Artificial Neural Network training and performance. In the past, one had to rely on experience and trial-and-error to find an adequate learning rate. Presently, a plethora of state of the art automatic methods exist that make the search for a good learning rate easier. While these techniques are effective and have yielded good results over the years, they are general solutions. This means the optimization of learning rate for specific network topologies remains largely unexplored. This work presents AutoLR, a framework that evolves Learning Rate Schedulers for a specific Neural Network Architecture using Structured Grammatical Evolution. The system was used to evolve learning rate policies that were compared with a commonly used baseline value for learning rate. Results show that training performed using certain evolved policies is more efficient than the established baseline and suggest that this approach is a viable means of improving a neural network's performance.

Developing secure machine learning models from adversarial examples is challenging as various methods are continually being developed to generate adversarial attacks. In this work, we propose an evolutionary approach to automatically determine Image Processing Techniques Sequence (IPTS) for detecting malicious inputs. Accordingly, we first used a diverse set of attack methods including adaptive attack methods (on our defense) to generate adversarial samples from the clean dataset. A detection framework based on a genetic algorithm (GA) is developed to find the optimal IPTS, where the optimality is estimated by different fitness measures such as Euclidean distance, entropy loss, average histogram, local binary pattern and loss functions. The "image difference" between the original and processed images is used to extract the features, which are then fed to a classification scheme in order to determine whether the input sample is adversarial or clean. This paper described our methodology and performed experiments using multiple data-sets tested with several adversarial attacks. For each attack-type and dataset, it generates unique IPTS. A set of IPTS selected dynamically in testing time which works as a filter for the adversarial attack. Our empirical experiments exhibited promising results indicating the approach can efficiently be used as processing for any AI model.

We apply the algorithm Neuroevolution learning literature [5, 6]. of Augmenting Topologies (NEAT) which has not been widely In this paper, we describe development and application of a applied generally in the credit evaluation domain. In addition to technique for learning online (or frequently updated) credit scoring comparing the methodology with other widely applied machine models as new data is read record by record. We describe the learning techniques, we develop and evaluate several approach developed as Online NEAT for Credit Scoring. The enhancements to the algorithm which make it suitable for the approach applies neuro-evolution, a technique that combines neural particular aspects of online learning that are relevant in the networks with evolutionary computation [7].

We combine momentum from machine learning with evolutionary dynamics, where momentum can be viewed as a simple mechanism of intergenerational memory. Using information divergences as Lyapunov functions, we show that momentum accelerates the convergence of evolutionary dynamics including the replicator equation and Euclidean gradient descent on populations. When evolutionarily stable states are present, these methods prove convergence for small learning rates or small momentum, and yield an analytic determination of the relative decrease in time to converge that agrees well with computations. The main results apply even when the evolutionary dynamic is not a gradient flow. We also show that momentum can alter the convergence properties of these dynamics, for example by breaking the cycling associated to the rock-paper-scissors landscape, leading to either convergence to the ordinarily non-absorbing equilibrium, or divergence, depending on the value and mechanism of momentum.

This top S&P 500 stocks forecast is designed for investors and analysts who need predictions for the whole S&P 500. Package Name: Top S&P 500 Stocks Recommended Positions: Long Forecast Length: 3 Months (4/1/2020 – 7/1/2020) I Know First Average: 32.03% The greatest return came from ABMD at 75.82%. NVDA and ETFC also performed well for this time horizon with returns of 44.61% and 42.98%, respectively. The overall average return in this Top S&P 500 Stocks package was 32.03%, providing investors with a 11.47% premium over the S&P 500's return of 20.56% during the same period.

This stock scanner is part of the Risk-Conscious Package, as one of I Know First's equity research solutions. We determine our aggressive stock picks by screening our algorithm daily for higher volatility stocks that present greater opportunities but are also riskier. Package Name: Aggressive Stocks Forecast Recommended Positions: Long Forecast Length: 3 Months (4/1/2020 – 7/1/2020) I Know First Average: 100.66% The highest trade return came from NVAX, at 486.89%. NLS and DPW followed with returns of 259.77% and 213.26% for the 3 Months period.

The theory of evolutionary computation for discrete search spaces has made a lot of progress during the last ten years. This survey summarizes some of the most important recent results obtained in this research area. It reviews important methods such as drift analysis, discusses theoretical insight on parameter tuning and parameter control, and summarizes the advances made for stochastic and dynamic problems. Furthermore, the survey highlights important results in the area of combinatorial optimization with a focus on parameterized complexity and the optimization of submodular functions. Finally, it gives an overview on the large amount of new important results for estimation of distribution algorithms.

Edmond de Belamy is a Generative Adversarial Network portrait painting constructed in 2018 by Paris-based arts-collective Obvious and sold for $432,500 in Southebys in October 2018.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Should you feel bad about pulling the plug on a robot or switch off an artificial intelligence algorithm? But how about when our computers become as smart--or smarter--than us? Debates about the consequences of artificial general intelligence (AGI) are almost as old as the history of AI itself. Most discussions depict the future of artificial intelligence as either Terminator-like apocalypse or Wall-E-like utopia.

In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer and third piezometer in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in the training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in the test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modeling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models.