Contextual policy search (CPS) is a class of multi-task reinforcement learning algorithms that is particularly useful for robotic applications. A recent state-of-the-art method is Contextual Covariance Matrix Adaptation Evolution Strategies (C-CMA-ES). It is based on the standard black-box optimization algorithm CMA-ES. There are two useful extensions of CMA-ES that we will transfer to C-CMA-ES and evaluate empirically: ACM-ES, which uses a comparison-based surrogate model, and aCMA-ES, which uses an active update of the covariance matrix. We will show that improvements with these methods can be impressive in terms of sample-efficiency, although this is not relevant any more for the robotic domain.

Purpose: Malicious web domain identification is of significant importance to the security protection of Internet users. With online credibility and performance data, this paper aims to investigate the use of machine learning tech-niques for malicious web domain identification by considering the class imbalance issue (i.e., there are more benign web domains than malicious ones). Design/methodology/approach: We propose an integrated resampling approach to handle class imbalance by combining the Synthetic Minority Over-sampling TEchnique (SMOTE) and Particle Swarm Optimisation (PSO), a population-based meta-heuristic algorithm. We use the SMOTE for over-sampling and PSO for under-sampling. Findings: By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain datasets with different imbalance ratios. Com-pared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications: This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains, but also provides an effective resampling approach for handling the class imbal-ance issue in the area of malicious web domain identification. Originality/value: Online credibility and performance data is applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class im-balance issue. The performance of the proposed approach is confirmed based on real-world datasets with different imbalance ratios.

Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, eps-MOEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications.

Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning). Automated algorithm configuration methods have recently received much attention in the AI community since they replace tedious, irreproducible and error-prone manual parameter tuning and can lead to new state-of-the-art performance. However, practical applications of algorithm configuration are prone to several (often subtle) pitfalls in the experimental design that can render the procedure ineffective. We identify several common issues and propose best practices for avoiding them. As one possibility for automatically handling as many of these as possible, we also propose a tool called GenericWrapper4AC.

To start implementing AI, you should have the basic knowledge of traditional algorithms and concepts. Artificial intelligence has been a thrill for the world's minds for decades. The quest for the creation of an artificial brain was inspired by the natural processes of the human brain. AI prototyping was represented in multiple science fiction books and movies. Gradually, the idea turned into a scientific concept and triggered the creation of practical intelligent technologies.

In a robotic mobile fulfillment system, robots bring shelves, called pods, with storage items from the storage area to pick stations. At every pick station there is a person -- the picker -- who takes parts from the pod and packs them into boxes according to orders. Usually there are multiple shelves at the pick station. In this case, they build a queue with the picker at its head. When the picker does not need the pod any more, a robot transports the pod back to the storage area. At that time, we need to answer a question: "Where is the optimal place in the inventory to put this pod back?". It is a tough question, because there are many uncertainties to consider before answering it. Moreover, each decision made to answer the question influences the subsequent ones. The goal of this paper is to answer the question properly. We call this problem the Pod Repositioning Problem and formulate a deterministic model. This model is tested with different algorithms, including binary integer programming, cheapest place, fixed place, random place, genetic algorithms, and a novel algorithm called tetris.

Can the origin of life be explained with quantum mechanics? And if so, are there quantum algorithms that could encode life itself? We're a little closer to finding out the answers to those big questions thanks to new research carried out with an IBM supercomputer. Encoding behaviours related to self-replication, mutation, interaction between individuals, and (inevitably) death, a newly created quantum algorithm has been used to show that quantum computers can indeed mimic some of the patterns of biology in the real world. This is still an early proof-of-concept prototype, but it opens the door to diving further into the relationship between quantum mechanics and the origins of life.

Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way. In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.

There is a larger number of vehicles in the streets The number of traffic jams is rising Tons of greenhouse gases are emitted to the atmosphere The citizens' quality of life is decreasing Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 1 / 73 5. Scientific and Technological Bases 6. Scientific and Technological Bases Smart Mobility Problems Smart Mobility Problems – The Challenge Long travel times Polluted cities Fuel economy Finding an available car park spot We are focused on Smart Mobility and Smart Environment Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 2 / 73 7. Scientific and Technological Bases Metaheuristics Metaheuristics Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 3 / 73 8. Scientific and Technological Bases Microsimulation Traffic Simulators Can be categorized as: Macroscopic Mesoscopic Microscopic After a deep study we selected SUMO (Simulation of Urban MObility) http://dlr.de/ts/sumo/ Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 4 / 73 9. Scientific and Technological Bases SUMO: Simulation of Urban MObility SUMO Open Source (German Aerospace Center - DLR) Several car following models Maps can be imported from OpenStreetMap Lots of data can be retrieved after the simulation Externally controlled by TraCI Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 5 / 73 10. Scientific and Technological Bases SUMO: Simulation of Urban MObility Building Mobility Scenarios with SUMO 1 Download the map from OpenStreetMap 2 Clean the irrelevant elements using JOSM 3 Import the city model using NETCONVERT 4 Define its routes using DUAROUTER We call it the experts' solution (computed by SUMO's DUAROUTER) Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 6 / 73 11. Scientific and Technological Bases Incomplete Maps and Data Incomplete Maps and Data PROBLEM: How reliable are the simulation scenarios? OUR PROPOSAL: Maps imported from OpenStreetMap Vehicular flows calculated according to data published by local councils Flow Generator Algorithm (FGA)* * Original contribution of this PhD thesis Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 7 / 73 12. Scientific and Technological Bases Incomplete Maps and Data Flow Generator Algorithm (FGA) Contributions: Flow Generator Algorithm Route Generator Set of mobility scenarios Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 8 / 73 13.

Memetic Computing is a subject in computer science which considers complex structures as the combination of simple agents, memes, whose evolutionary interactions lead to intelligent structures capable of problem-solving. This paper focuses on Memetic Computing optimization algorithms and proposes a counter-tendency approach for algorithmic design. Research in the field tends to go in the direction of improving existing algorithms by combining different methods or through the formulation of more complicated structures. Contrary to this trend, we instead focus on simplicity, proposing a structurally simple algorithm with emphasis on processing only one solution at a time. The proposed algorithm, namely Three Stage Optimal Memetic Exploration, is composed of three memes; the first stochastic and with a long search radius, the second stochastic and with a moderate search radius and the third deterministic and with a short search radius. This is suggestive of the fact that complexity in algorithmic structures can be unnecessary, if not detrimental, and that simple bottom-up approaches are likely to be competitive is here invoked as an extension to Memetic Computing basing on the philosophical concept of Ockham's Razor. An extensive experimental setup on various test problems and one digital signal processing application is presented. Numerical results show that the proposed approach, despite its simplicity and low computational cost displays a very good performance on several problems, and is competitive with sophisticated algorithms representing the-state-of-the-art in computational intelligence optimization. Key words: Memetic Computing, Evolutionary Algorithms, Memetic Algorithms, Computational intelligence Optimization 1. Introduction Emerging technologies in computer science and engineering, as well as the demands of the market and the society, often impose the solution, in the every day life, of complex optimization problems. The complexity of today's problems is due to various reasons such as high non-linearities, high multi-modality, large scale, noisy fitness landscape, computationally expensive fitness functions, real-time demands, and limited hardware available(e.g. when the computational device is portable and cheap). In these cases, the use of exact methods is unsuitable because, in general, there is not sufficient prior knowledge (hypotheses) on the optimization problem; thus, computational intelligence approaches become not only advisable but often the only alternative to face the optimization. Scientific research in computational intelligence optimization can be classified into two general categories.