As Charles Darwin wrote in at the end of his seminal 1859 book On the Origin of the Species, "whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved." Scientists have since long believed that the diversity and range of forms of life on Earth provide evidence that biological evolution spontaneously innovates in an open-ended way, constantly inventing new things. However, attempts to construct artificial simulations of evolutionary systems tend to run into limits in the complexity and novelty which they can produce. This is sometimes referred to as "the problem of open-endedness." Because of this difficulty, to date, scientists can't easily make artificial systems capable of exhibiting the richness and diversity of biological systems.

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In~this~ article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using \textit{Particle Swarm Optimization}. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field

This article describes the application of soft computing methods for solving the problem of locating garbage accumulation points in urban scenarios. This is a relevant problem in modern smart cities, in order to reduce negative environmental and social impacts in the waste management process, and also to optimize the available budget from the city administration to install waste bins. A specific problem model is presented, which accounts for reducing the investment costs, enhance the number of citizens served by the installed bins, and the accessibility to the system. A family of single- and multi-objective heuristics based on the PageRank method and two mutiobjective evolutionary algorithms are proposed. Experimental evaluation performed on real scenarios on the cities of Montevideo (Uruguay) and Bahia Blanca (Argentina) demonstrates the effectiveness of the proposed approaches. The methods allow computing plannings with different trade-off between the problem objectives. The computed results improve over the current planning in Montevideo and provide a reasonable budget cost and quality of service for Bahia Blanca.

As the capabilities and pervasiveness of machine learning (ML) and artificial intelligence (AI) increasingly affect society, there is increasing concern about the safety of such systems, i.e. the potential of accidental harm from implementation errors and unintended consequences in ML algorithms. As a result, there has been increasing interest in the nascent field of AI safety [1, 2, 3, 4, 5, 6], which seeks to understand and solve the technical challenges in developing and deploying AI that does what it is intended to do. The purpose of this chapter is to explore how the study of AI safety intersects with that of evolutionary computation (EC), to both highlight an exciting and important set of safety problems within EC, and to suggest that evolution and EC have important insights that could benefit the general study of AI safety. To frame the problem of AI safety, we adopt the framework of Amodei et al. [1], which defines AI safety as concerned with accidents in ML systems, and defines five problems within three broad categories of issues: (1) specifying the wrong objective function, (2) making safe and efficient use of a true but expensive objective (e.g.

Reinforcement Learning (RL) has recently achieved tremendous success due to the partnership with Deep Neural Networks (DNNs). Genetic Algorithms (GAs), often seen as a competing approach to RL, have run out of favour due to their inability to scale up to the DNNs required to solve the most complex environments. Contrary to this dichotomic view, in the physical world, evolution and learning are complementary processes that continuously interact. The recently proposed Evolutionary Reinforcement Learning (ERL) framework has demonstrated the capacity of the two methods to enhance each other. However, ERL has not fully addressed the scalability problem of GAs. In this paper, we argue that this problem is rooted in an unfortunate combination of a simple genetic encoding for DNNs and the use of traditional biologically-inspired variation operators. When applied to these encodings, the standard operators are destructive and cause catastrophic forgetting of the traits the networks acquired. We propose a novel algorithm called Proximal Distilled Evolutionary Reinforcement Learning (PDERL) that is characterised by a hierarchical integration between evolution and learning. The main innovation of PDERL is the use of learning-based variation operators that compensate for the simplicity of the genetic representation. Unlike the traditional operators, the ones we propose meet their functional requirements. We evaluate PDERL in five robot locomotion environments from the OpenAI gym. Our method outperforms ERL, as well as two state of the art RL algorithms, PPO and TD3, in all the environments.

Whether it is the Irish backstop or English Channel, the issue of how the UK and Europe are controlling their borders has been thrust into the public consciousness. And as with many of the globe's conundrums, countries and private companies are turning to ever more futuristic, and often controversial, technologies in order to protect their borders. There are, of course, immediate issues for Britain's borders with quandaries such as the potential hard border in Northern Ireland following Brexit, with the nebulous'technology' promised by some politicians either still being developed or put under question. One such future proposal is a satellite system that registered mobile phones as they pass the border, while sensors buried in the ground or radars on flying drones could detect possible unlawful breaches of the boundaries. But that would still leave the question of invasive, even if largely invisible, checks that run against the Good Friday Agreement.

In a new study published in the journal Artificial Life, a research team led by Nicholas Guttenberg and Nathaniel Virgo of the Earth-Life Science Institute (ELSI) at Tokyo Institute of Technology, Japan, and Alexandra Penn of The Centre for Evaluation of Complexity Across the Nexus (CECAN), University of Surrey UK (CRESS), examine the connection between biological evolutionary open-endedness and recent studies in machine learning, hoping that by connecting ideas from artificial life and machine learning, it will become possible to combine neural networks with the motivations and ideas of artificial life to create new forms of open-endedness. One source of open-endedness in evolving biological systems is an "arms race" for survival. For example, faster foxes may evolve to catch faster rabbits, which in turn may evolve to become even faster to get away from the faster foxes. This idea is mirrored in recent developments involving placing networks in competition with each other to produce things such as realistic images using generative adversarial networks (GANs), and to discover strategies in games such as Go, which can now easily beat top human players. In evolution, factors such as mutation can limit the extent of an arms race.

What is happening in the field of evolutionary and genetic algorithms today? Are there any cutting edge scientific projects in terms of AI/AGI? I'd very much appreciate it if someone could link me the relevant websites, researches, papers regarding the subject along with respective books or monographs. I'm just trying to find things out and getting back on track.

With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tackle this problem, we introduce the TitAnt, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech companies in the world. The system is able to predict online real-time transaction fraud in mere milliseconds. We present the problem definition, feature extraction, detection methods, implementation and deployment of the system, as well as empirical effectiveness. Extensive experiments have been conducted on large real-world transaction data to show the effectiveness and the efficiency of the proposed system.

Comprehensive quality-aware automated semantic web service composition is an NP-hard problem, where service composition workflows are unknown, and comprehensive quality, i.e., Quality of services (QoS) and Quality of semantic matchmaking (QoSM) are simultaneously optimized. The objective of this problem is to find a solution with optimized or near-optimized overall QoS and QoSM within polynomial time over a service request. In this paper, we proposed novel memetic EDA-based approaches to tackle this problem. The proposed method investigates the effectiveness of several neighborhood structures of composite services by proposing domain-dependent local search operators. Apart from that, a joint strategy of the local search procedure is proposed to integrate with a modified EDA to reduce the overall computation time of our memetic approach. To better demonstrate the effectiveness and scalability of our approach, we create a more challenging, augmented version of the service composition benchmark based on WSC-08 \cite{bansal2008wsc} and WSC-09 \cite{kona2009wsc}. Experimental results on this benchmark show that one of our proposed memetic EDA-based approach (i.e., MEEDA-LOP) significantly outperforms existing state-of-the-art algorithms.