The entropy models have been recently adopted in many studies to evaluate the distribution of the shear stress in circular channels. However, the uncertainty in their predictions and their reliability remains an open question. We present a novel method to evaluate the uncertainty of four popular entropy models, including Shannon, Shannon-Power Low (PL), Tsallis, and Renyi, in shear stress estimation in circular channels. The Bayesian Monte-Carlo (BMC) uncertainty method is simplified considering a 95% Confidence Bound (CB). We developed a new statistic index called as FREEopt-based OCB (FOCB) using the statistical indices Forecasting Range of Error Estimation (FREE) and the percentage of observed data in the CB (Nin), which integrates their combined effect. The Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, had the highest certainty in the calculation of shear stress values in circular channels followed by traditional uniform flow shear stress and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy with much higher values of FOCB equal to 57.726 has less certainty in the estimation of shear stress than other models. Using the presented results in this study, the amount of confidence in entropy methods in the calculation of shear stress to design and implement different types of open channels and their stability is determined.

In a disaster situation, air ambulance rapid response will often be the determining factor in patient survival. Obstacles intensify this circumstance, with geographical remoteness and limitations in vehicle placement making it an arduous task. Considering these elements, the arrangement of responders is a critical decision of the utmost importance. Utilizing real mission data, this research structured an optimal coverage problem with integer linear programming. For accurate comparison, the Gurobi optimizer was programmed with the developed model and timed for performance. A solution implementing base ranking followed by both local and Tabu search-based algorithms was created. The local search algorithm proved insufficient for maximizing coverage, while the Tabu search achieved near-optimal results. In the latter case, the total vehicle travel distance was minimized and the runtime significantly outperformed the one generated by Gurobi. Furthermore, variations utilizing parallel CUDA processing further decreased the algorithmic runtime. These proved superior as the number of test missions increased, while also maintaining the same minimized distance.

Under Frequency Fitness Assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in fitness assignment steps and is subject to minimization. FFA renders optimization processes invariant under bijective transformations of the objective function. This is the strongest invariance property of any optimization procedure to our knowledge. On TwoMax, Jump, and Trap functions of scale s, a (1+1)-EA with standard mutation at rate 1/s can have expected running times exponential in s. In our experiments, a (1+1)-FEA, the same algorithm but using FFA, exhibits mean running times quadratic in s. Since Jump and Trap are bijective transformations of OneMax, it behaves identical on all three. On the LeadingOnes and Plateau problems, it seems to be slower than the (1+1)-EA by a factor linear in s. The (1+1)-FEA performs much better than the (1+1)-EA on W-Model and MaxSat instances. Due to the bijection invariance, the behavior of an optimization algorithm using FFA does not change when the objective values are encrypted. We verify this by applying the Md5 checksum computation as transformation to some of the above problems and yield the same behaviors. Finally, FFA can improve the performance of a Memetic Algorithm for Job Shop Scheduling.

Transportation occupies one-third of the amount in the logistics costs, and accordingly transportation systems largely influence the performance of the logistics system. This work presents an adaptive data-driven innovative modular approach for solving the real-world Vehicle Routing Problems (VRP) in the field of logistics. The work consists of two basic units: (i) an innovative multi-step algorithm for successful and entirely feasible solving of the VRP problems in logistics, (ii) an adaptive approach for adjusting and setting up parameters and constants of the proposed algorithm. The proposed algorithm combines several data transformation approaches, heuristics and Tabu search. Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. A comparison of the acquired results has been made using the Decision Support System with predictive models: Generalized Linear Models (GLM) and Support Vector Machine (SVM). The algorithm, along with the control parameters, which using the prediction method were acquired, was incorporated into a web-based enterprise system, which is in use in several big distribution companies in Bosnia and Herzegovina. The results of the proposed algorithm were compared with a set of benchmark instances and validated over real benchmark instances as well. The successful feasibility of the given routes, in a real environment, is also presented.

--In this paper, we evolve a card-choice strategy for the arena mode of Legends of Code and Magic, a programming game inspired by popular collectible card games like Hearthstone or TES: Legends. In the arena game mode, before each match, a player has to construct his deck choosing cards one by one from the previously unknown options. Such a scenario is difficult from the optimization point of view, as not only the fitness function is non-deterministic, but its value, even for a given problem instance, is impossible to be calculated directly and can only be estimated with simulation-based approaches. We propose a variant of the evolutionary algorithm that uses a concept of an active gene to reduce the range of the operators only to generation-specific subsequences of the genotype. Thus, we batched learning process and constrained evolutionary updates only to the cards relevant for the particular draft, without forgetting the knowledge from the previous tests. We developed and tested various implementations of this idea, investigating their performance by taking into account the computational cost of each variant. Performed experiments show that some of the introduced active-genes algorithms tend to learn faster and produce statistically better draft policies than the compared methods. I NTRODUCTION Currently, not only classical boardgames like Chess [1] and Go [2] are used as grand challenges for AI research. It has been recently shown that such a role may be taken by modern computer games. So far presented approaches that beat the best human players in Dota 2 [3] and StarCraft II [4], are one of the most spectacular and media-impacting demonstrations of AI capabilities. The weight is put on particular game features that make designing successful AI players especially tricky, e.g., imperfect information, randomness, long term planning, and massive action space.

From Two Minute Papers, an amazing look at 4 instances where artificial intelligence outsmarted the folk who developed it. Makes one think …... or, do we really have to (think)? The paper "The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities" is available here: https://arxiv.org/abs/1803.03453

Much of model-based reinforcement learning involves learning a model of an agent's world, and training an agent to leverage this model to perform a task more efficiently. While these models are demonstrably useful for agents, every naturally occurring model of the world of which we are aware---e.g., a brain---arose as the byproduct of competing evolutionary pressures for survival, not minimization of a supervised forward-predictive loss via gradient descent. That useful models can arise out of the messy and slow optimization process of evolution suggests that forward-predictive modeling can arise as a side-effect of optimization under the right circumstances. Crucially, this optimization process need not explicitly be a forward-predictive loss. In this work, we introduce a modification to traditional reinforcement learning which we call observational dropout, whereby we limit the agents ability to observe the real environment at each timestep. In doing so, we can coerce an agent into learning a world model to fill in the observation gaps during reinforcement learning. We show that the emerged world model, while not explicitly trained to predict the future, can help the agent learn key skills required to perform well in its environment. Videos of our results available at https://learningtopredict.github.io/

There is a growing interest and literature on intrinsic motivations and open-ended learning in both cognitive robotics and machine learning on one side, and in psychology and neuroscience on the other. This paper aims to review some relevant contributions from the two literature threads and to draw links between them. To this purpose, the paper starts by defining intrinsic motivations and by presenting a computationally-driven theoretical taxonomy of their different types. Then it presents relevant contributions from the psychological and neuroscientific literature related to intrinsic motivations, interpreting them based on the grid, and elucidates the mechanisms and functions they play in animals and humans. Endowed with such concepts and their biological underpinnings, the paper next presents a selection of models from cognitive robotics and machine learning that computationally operationalise the concepts of intrinsic motivations and links them to biology concepts. The contribution finally presents some of the open challenges of the field from both the psychological/neuroscientific and computational perspectives.

This paper proposes Evolutionary Multi-objective Optimization (EMO)-based Adversarial Example (AE) design method that performs under black-box setting. Previous gradient-based methods produce AEs by changing all pixels of a target image, while previous EC-based method changes small number of pixels to produce AEs. Thanks to EMO's property of population based-search, the proposed method produces various types of AEs involving ones locating between AEs generated by the previous two approaches, which helps to know the characteristics of a target model or to know unknown attack patterns. Experimental results showed the potential of the proposed method, e.g., it can generate robust AEs and, with the aid of DCT-based perturbation pattern generation, AEs for high resolution images.

Highly non-linear machine learning algorithms have the capacity to handle large, complex datasets. However, the predictive performance of a model usually critically depends on the choice of multiple hyperparameters. Optimizing these (often) constitutes an expensive black-box problem. Model-based optimization is one state-of-the-art method to address this problem. Furthermore, resulting models often lack interpretability, as models usually contain many active features with non-linear effects and higher-order interactions. One model-agnostic way to enhance interpretability is to enforce sparse solutions through feature selection. It is in many applications desirable to forego a small drop in performance for a substantial gain in sparseness, leading to a natural treatment of feature selection as a multi-objective optimization task. Despite the practical relevance of both hyperparameter optimization and feature selection, they are often carried out separately from each other, which is neither efficient, nor does it take possible interactions between hyperparameters and selected features into account. We present, discuss and compare two algorithmically different approaches for joint and multi-objective hyperparameter optimization and feature selection: The first uses multi-objective model-based optimization to tune a feature filter ensemble. The second is an evolutionary NSGA-II-based wrapper-approach to feature selection which incorporates specialized sampling, mutation and recombination operators for the joint decision space of included features and hyperparameter settings. We compare and discuss the approaches on a variety of benchmark tasks. While model-based optimization needs fewer objective evaluations to achieve good performance, it incurs significant overhead compared to the NSGA-II-based approach. The preferred choice depends on the cost of training the ML model on the given data.