Abstract-The fitness landscape of MAX-3-SAT is investigated for random instances above the satisfiability phase transition. This study includes a scaling analysis of the time to reach a local optimum, the number of local optima, the expected probability of reaching a local optimum as a function of its fitness, the expected fitness found by local search and the best fitness, the probability of reaching a global optimum, the size and relative positions of the global optima, the mean distance between the local and global optima, the expected fitness as a function of the Hamming distance from an optimum and their basins of attraction. These analyses show why the problem becomes hard for local search algorithms as the system size increases. The paper also shows how a recently proposed algorithm can exploit long-range correlations in the fitness landscape to improve on the state-of-the-art heuristic algorithms.

The nervous systems converts the physical quantities sensed by its primary receptors into trains of events that are then processed in the brain. The unmatched efficiency in information processing has long inspired engineers to seek brain-like approaches to sensing and signal processing. The key principle pursued in neuromorphic sensing is to shed the traditional approach of periodic sampling in favor of an event-driven scheme that mimicks sampling as it occurs in the nervous system, where events are preferably emitted upon the change of the sensed stimulus. In this paper we highlight the advantages and challenges of event-based sensing and signal processing in the visual, auditory and olfactory domains. We also provide a survey of the literature covering neuromorphic sensing and signal processing in all three modalities. Our aim is to facilitate research in event-based sensing and signal processing by providing a comprehensive overview of the research performed previously as well as highlighting conceptual advantages, current progress and future challenges in the field.

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The spread of the COVID-19 disease has prompted a need for immediate reaction by governments to curb the pandemic. Many countries have adopted different policies and studies are performed to understand the effect of each of the policies on the growth rate of the infected cases. In this paper, the data about the policies taken by all countries at each date, and the effect of the policies on the growth rate of the pandemic are used to build a model of the pandemic's behaviour. The model takes as input a set of policies and predicts the growth rate of the pandemic. Then, a population-based multi-objective optimisation algorithm is developed which uses the model to search through the policy space and finds a set of policies that minimise the cost induced to the society due to the policies and the growth rate of the pandemic. Because of the complexity of the modelling problem and the uncertainty in measuring the growth rate of the pandemic via the models, an ensemble learning algorithm is proposed in this paper to improve the performance of individual learning algorithms. The ensemble consists of ten learning algorithms and a meta-model algorithm that is built to predict the accuracy of each learning algorithm for a given data record. The meta-model is a set of Support Vector Machine (SVM) algorithms that is used in the aggregation phase of the ensemble algorithm. Because there is uncertainty in measuring the growth rate via the models, a landscape smoothing operator is proposed in the optimisation process which aims at reducing uncertainty. The algorithm is tested on open access data online and experiments on the ensemble learning and the policy optimisation algorithms are performed.

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