Measuring public opinion is a key focus during democratic elections, enabling candidates to gauge their popularity and alter their campaign strategies accordingly. Traditional survey polling remains the most popular estimation technique, despite its cost and time intensity, measurement errors, lack of real-time capabilities and lagged representation of public opinion. In recent years, Twitter opinion mining has attempted to combat these issues. Despite achieving promising results, it experiences its own set of shortcomings such as an unrepresentative sample population and a lack of long term stability. This paper aims to merge data from both these techniques using Bayesian data assimilation to arrive at a more accurate estimate of true public opinion for the Brexit referendum. This paper demonstrates the effectiveness of the proposed approach using Twitter opinion data and survey data from trusted pollsters. Firstly, the possible existence of a time gap of 16 days between the two data sets is identified. This gap is subsequently incorporated into a proposed assimilation architecture. This method was found to adequately incorporate information from both sources and measure a strong upward trend in Leave support leading up to the Brexit referendum. The proposed technique provides useful estimates of true opinion, which is essential to future opinion measurement and forecasting research.

New autonomous driving technologies are emerging every day and some of them have been commercially applied in the real world. While benefiting from these technologies, autonomous trucks are facing new challenges in short-term maintenance planning, which directly influences the truck operator's profit. In this paper, we implement a vehicle health management system by addressing the maintenance planning issues of autonomous trucks on a transport mission. We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company. Both availability losses and maintenance costs are considered when evaluating the economic risk. We demonstrate the proposed model by numerical experiments illustrating real-world scenarios. In the experiments, compared to three baseline methods, the expected economic risk of the proposed method is reduced by up to $47\%$. We also conduct sensitivity analyses of different model parameters. The analyses show that the economic risk significantly decreases when the estimation accuracy of remaining useful life, the maximal allowed time of delivery delay before order cancellation, or the number of workshops increases. The experiment results contribute to identifying future research and development attentions of autonomous trucks from an economic perspective.

Generative Adversarial Networks (GANs) are a powerful indirect genotype-to-phenotype mapping for evolutionary search, but they have limitations. In particular, GAN output does not scale to arbitrary dimensions, and there is no obvious way to combine GAN outputs into a cohesive whole, which would be useful in many areas, such as video game level generation. Game levels often consist of several segments, sometimes repeated directly or with variation, organized into an engaging pattern. Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs). Specifically, a CPPN can define latent vector GAN inputs as a function of geometry, which provides a way to organize level segments output by a GAN into a complete level. However, a collection of latent vectors can also be evolved directly, to produce more chaotic levels. Here, we propose a new hybrid approach that evolves CPPNs first, but allows the latent vectors to evolve later, and combines the benefits of both approaches. These approaches are evaluated in Super Mario Bros. and The Legend of Zelda. We previously demonstrated via divergent search (MAP-Elites) that CPPNs better cover the space of possible levels than directly evolved levels. Here, we show that the hybrid approach can cover areas that neither of the other methods can and achieves comparable or superior QD scores.

Following a similar principle--called "swarm learning"--an international research team has trained artificial intelligence algorithms to detect blood cancer, lung diseases and COVID-19 in data stored in a decentralized fashion. This approach has advantage over conventional methods since it inherently provides privacy preservation technologies, which facilitates cross-site analysis of scientific data. Swarm learning could thus significantly promote and accelerate collaboration and information exchange in research, especially in the field of medicine. Experts from the DZNE, the University of Bonn, the information technology company Hewlett Packard Enterprise (HPE) and other research institutions report on this in the scientific journal Nature. Science and medicine are becoming increasingly digital.

We propose a new technique for the automatic generation of optimal ad-hoc ans\"atze for classification by using quantum support vector machine (QSVM). This efficient method is based on NSGA-II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning.

Optimal Lens Design constitutes a fundamental, long-standing real-world optimization challenge. Potentially large number of optima, rich variety of critical points, as well as solid understanding of certain optimal designs per simple problem instances, provide altogether the motivation to address it as a niching challenge. This study applies established Niching-CMA-ES heuristic to tackle this design problem (6-dimensional Cooke triplet) in a simulation-based fashion. The outcome of employing Niching-CMA-ES `out-of-the-box' proves successful, and yet it performs best when assisted by a local searcher which accurately drives the search into optima. The obtained search-points are corroborated based upon concrete knowledge of this problem-instance, accompanied by gradient and Hessian calculations for validation. We extensively report on this computational campaign, which overall resulted in (i) the location of 19 out of 21 known minima within a single run, (ii) the discovery of 540 new optima. These are new minima similar in shape to 21 theoretical solutions, but some of them have better merit function value (unknown heretofore), (iii) the identification of numerous infeasibility pockets throughout the domain (also unknown heretofore). We conclude that niching mechanism is well-suited to address this problem domain, and hypothesize on the apparent multidimensional structures formed by the attained new solutions.

Historically, most evolutionary algorithms (EAs) were designed to optimise a fitness function, solving a single problem without considerations for generalisation to unseen problems or robustness to perturbations to the evaluation environment. However, it was widely known that successfully converging to the maximum of that fitness function requires maintaining genetic diversity in the population of solutions (see e.g., Laumanns et al. (2002); Gupta and Ghafir (2012); Ursem (2002); Ginley et al. (2011)). Moreover, the use of niching demonstrated how maintaining subpopulations could help find multiple solutions to a single problem (Mahfoud, 1995). Some studies included genetic diversity as one of the objectives of the EA (Toffolo and Benini, 2003). Approaches in evolutionary robotics, artificial life, and neuro-evolution realised that genetic diversity does not necessarily imply a diversity of solutions, since (i) different genotypes may encode the same behaviour and vice versa (especially for complex genotypes such as neural networks); and (ii) many genotypes may encode unsafe or undesirable solutions that should be discarded during evolution (e.g., self-collisions on a multi-joint robot arm). Such approaches began to emphasise behavioural diversity (Mouret and Doncieux, 2009b; Gomez, 2009; Mouret and Doncieux, 2009a; Mouret, 2010), not only as a driver for objective-based evolution but also as the enabler for diversity-or novelty-driven evolution (Lehman and Stanley, 2011a). This work is the extended version of the paper: David M. Bossens & Danesh Tarapore (2021). On the use of feature-maps for improved quality-diversity meta-evolution.

Dynamic Algorithm Configuration (DAC) aims to dynamically control a target algorithm's hyperparameters in order to improve its performance. Several theoretical and empirical results have demonstrated the benefits of dynamically controlling hyperparameters in domains like evolutionary computation, AI Planning or deep learning. Replicating these results, as well as studying new methods for DAC, however, is difficult since existing benchmarks are often specialized and incompatible with the same interfaces. To facilitate benchmarking and thus research on DAC, we propose DACBench, a benchmark library that seeks to collect and standardize existing DAC benchmarks from different AI domains, as well as provide a template for new ones. For the design of DACBench, we focused on important desiderata, such as (i) flexibility, (ii) reproducibility, (iii) extensibility and (iv) automatic documentation and visualization. To show the potential, broad applicability and challenges of DAC, we explore how a set of six initial benchmarks compare in several dimensions of difficulty.

In addition to their undisputed success in solving classical optimization problems, neuroevolutionary and population-based algorithms have become an alternative to standard reinforcement learning methods. However, evolutionary methods often lack the sample efficiency of standard value-based methods that leverage gathered state and value experience. If reinforcement learning for real-world problems with significant resource cost is considered, sample efficiency is essential. The enhancement of evolutionary algorithms with experience exploiting methods is thus desired and promises valuable insights. This work presents a hybrid algorithm that combines topology-changing neuroevolutionary optimization with value-based reinforcement learning. We illustrate how the behavior of policies can be used to create distance and loss functions, which benefit from stored experiences and calculated state values. They allow us to model behavior and perform a directed search in the behavior space by gradient-free evolutionary algorithms and surrogate-based optimization. For this purpose, we consolidate different methods to generate and optimize agent policies, creating a diverse population. We exemplify the performance of our algorithm on standard benchmarks and a purpose-built real-world problem. Our results indicate that combining methods can enhance the sample efficiency and learning speed for evolutionary approaches.

Swarm intelligence is a natural step in the evolution of certain social species. It explains why ants colonize, bees swarm, fish school and birds flock. Nature has proven that when individual creatures collaboratively work and think together as unified systems toward a common goal, they're more likely to reach that goal faster and more accurately than if they were to attempt it individually. In other words, they're smarter together than they are on their own. Swarm intelligence is the collective behavior of decentralized, self-organized systems (natural or artificial) that can maneuver quickly in a coordinated fashion. In nature, this closed-loop, collaborative behavior is unique within each species.