Exploration is a key challenge in Reinforcement Learning, especially in long-horizon, deceptive and sparse-reward environments. For such applications, population-based approaches have proven effective. Methods such as Quality-Diversity deals with this by encouraging novel solutions and producing a diversity of behaviours. However, these methods are driven by either undirected sampling (i.e. mutations) or use approximated gradients (i.e. Evolution Strategies) in the parameter space, which makes them highly sample-inefficient. In this paper, we propose a model-based Quality-Diversity approach. It extends existing QD methods to use gradients for efficient exploitation and leverage perturbations in imagination for efficient exploration. Our approach optimizes all members of a population simultaneously to maintain both performance and diversity efficiently by leveraging the effectiveness of QD algorithms as good data generators to train deep models. We demonstrate that it maintains the divergent search capabilities of population-based approaches on tasks with deceptive rewards while significantly improving their sample efficiency and quality of solutions.

Contemporary approaches to swarm guidance and control often assume that swarm agents are homogeneous in their response to external influence vectors. This manifests in the design of control algorithms, such as herding, often operating directly on the raw positional data of swarm agents to compute influence vectors. Herding-based models, such as shepherding, have been implemented for over 25 years, with classic control methods typically operating on simple transformations of raw data Hasan, Baxter, Castillo, Delgado, and Tapia (2022). Swarm shepherding is an example of a swarm control herdingbased method where one or more external actuators (sheepdogs) operate on low-level information by calculating primitive statistical features from raw data. These models often use static behaviour selection policies for the control agent to guide a swarm to a goal location Debie et al. (2021). As a biologically-inspired approach to swarm control, shepherding has applications across different domains, such as the guidance and control of crowds Li, Hu, Liang, and Li (2012), herding biological animals Paranjape, Chung, Kim, and Shim (2018), guiding teams of uncrewed system (UxS) Hepworth (2021), and controlling a group of robotic platforms Cowling and Gmeinwieser (2010); Lee and Kim (2017).

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners.

This paper addresses the Capacitated Arc Routing Problem (CARP) using an Ant Colony Optimization scheme. Ant Colony schemes can compute solutions for medium scale instances of VRP. The proposed Ant Colony is dedicated to large-scale instances of CARP with more than 140 nodes and 190 arcs to service. The Ant Colony scheme is coupled with a local search procedure and provides high quality solutions. The benchmarks we carried out prove possible to obtain solutions as profitable as CARPET ones can be obtained using such scheme when a sufficient number of iterations is devoted to the ants. It competes with the Genetic Algorithm of Lacomme et al. regarding solution quality but it is more time consuming on large scale instances. The method has been intensively benchmarked on the well-known instances of Eglese, DeArmon and the last ones of Belenguer and Benavent.

Through ML we can address SE problems that cannot be completely algorithmically modeled, or for which existing solutions do not provide satisfactory results yet (e.g., defect/fault detection [16, 165, 180]). In addition, ML finds application in SE tasks where data cannot be easily analyzed with other algorithms (e.g., software requirements, code comments, code reviews, issues [9, 91, 174]). Another important aspect of ML is that it can significantly reduce manual effort in common SE tasks (e.g., automatic program repair [157], code suggestion [61], defect prediction [19], malware detection [147], feature location [40]) with great accuracy results [146, 164]. In fields such as health informatics ML and SE are considered complementary disciplines, since the growing scale and complexity of healthcare datasets have posed a challenge for clinical practice and medical research, requiring new engineering approaches from both fields [38]. In the early nineties, Huff and Selfridge [68] recognized the need for creating software systems that partially take some responsibility for their own evolution, offering the ability to implement, measure, and assess changes easily. These changes should also contribute to the overall improvement of the corresponding systems [142].

Standard evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward and computationally cheap. However, in many real-world optimization problems, these evaluations involve computationally expensive numerical simulations or physical experiments. Surrogate-assisted evolutionary algorithms (SAEAs) have recently gained increased attention for their performance in solving these types of problems. The main idea of SAEAs is the integration of an evolutionary algorithm with a selected surrogate model that approximates the computationally expensive function. In this paper, we propose a surrogate model based on a Lipschitz underestimation and use it to develop a differential evolution-based algorithm. The algorithm, called Lipschitz Surrogate-assisted Differential Evolution (LSADE), utilizes the Lipschitz-based surrogate model, along with a standard radial basis function surrogate model and a local search procedure. The experimental results on seven benchmark functions of dimensions 30, 50, 100, and 200 show that the proposed LSADE algorithm is competitive compared with the state-of-the-art algorithms under a limited computational budget, being especially effective for the very complicated benchmark functions in high dimensions.

Thanks to the advantages of flexible deployment and high mobility, unmanned aerial vehicles (UAVs) have been widely applied in the areas of disaster management, agricultural plant protection, environment monitoring and so on. With the development of UAV and sensor technologies, UAV assisted data collection for Internet of Things (IoT) has attracted increasing attentions. In this article, the scenarios and key technologies of UAV assisted data collection are comprehensively reviewed. First, we present the system model including the network model and mathematical model of UAV assisted data collection for IoT. Then, we review the key technologies including clustering of sensors, UAV data collection mode as well as joint path planning and resource allocation. Finally, the open problems are discussed from the perspectives of efficient multiple access as well as joint sensing and data collection. This article hopefully provides some guidelines and insights for researchers in the area of UAV assisted data collection for IoT.

Multitask learning assumes that models capable of learning from multiple tasks can achieve better quality and efficiency via knowledge transfer, a key feature of human learning. Though, state of the art ML models rely on high customization for each task and leverage size and data scale rather than scaling the number of tasks. Also, continual learning, that adds the temporal aspect to multitask, is often focused to the study of common pitfalls such as catastrophic forgetting instead of being studied at a large scale as a critical component to build the next generation artificial intelligence.We propose an evolutionary method capable of generating large scale multitask models that support the dynamic addition of new tasks. The generated multitask models are sparsely activated and integrates a task-based routing that guarantees bounded compute cost and fewer added parameters per task as the model expands.The proposed method relies on a knowledge compartmentalization technique to achieve immunity against catastrophic forgetting and other common pitfalls such as gradient interference and negative transfer. We demonstrate empirically that the proposed method can jointly solve and achieve competitive results on 69public image classification tasks, for example improving the state of the art on a competitive benchmark such as cifar10 by achieving a 15% relative error reduction compared to the best model trained on public data.

All cognitive agents are composite beings. Specifically, complex living agents consist of cells, which are themselves competent sub-agents navigating physiological and metabolic spaces. Behavior science, evolutionary developmental biology, and the field of machine intelligence all seek an answer to the scaling of biological cognition: what evolutionary dynamics enable individual cells to integrate their activities to result in the emergence of a novel, higher-level intelligence that has goals and competencies that belong to it and not to its parts? Here, we report the results of simulations based on the TAME framework, which proposes that evolution pivoted the collective intelligence of cells during morphogenesis of the body into traditional behavioral intelligence by scaling up the goal states at the center of homeostatic processes. We tested the hypothesis that a minimal evolutionary framework is sufficient for small, low-level setpoints of metabolic homeostasis in cells to scale up into collectives (tissues) which solve a problem in morphospace: the organization of a body-wide positional information axis (the classic French Flag problem). We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve its target morphology as well as the ability to recover from perturbation (robustness) and long-term stability (even though neither of these was directly selected for). Moreover we observed unexpected behavior of sudden remodeling long after the system stabilizes. We tested this prediction in a biological system - regenerating planaria - and observed a very similar phenomenon. We propose that this system is a first step toward a quantitative understanding of how evolution scales minimal goal-directed behavior (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.

This forecast is part of the Dividends Package, as one of I Know First's quantitative investment solutions. We determine the best stocks carrying a dividend by screening our database daily using our advanced algorithm. Package Name: Dividend Stocks Forecast Recommended Positions: Long Forecast Length: 1 Month (10/13/22 – 11/13/22) I Know First Average: 24.92% For this 1 Month forecast the algorithm has successfully predicted 10 out of 10 movements. The top-performing prediction in this forecast was APPS, which registered a return of 44.18%.