Cooperative co-evolution for feature selection in big data with random feature grouping

© 2020, The Author(s). A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because of some limitations, such as not considering feature interactions, dealing with only an even number of features, and decomposing the dataset statically. In this paper, a novel random feature grouping (RFG) has been introduced with its three variants to dynamically decompose Big Data datasets and to ensure the probability of grouping interacting features into the same subcomponent. RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG). Experiment analysis was performed using six widely used ML classifiers on seven different datasets from the UCI ML repository and Princeton University Genomics repository with and without FS. The experimental results indicate that in most cases [i.e., with naïve Bayes (NB), support vector machine (SVM), k-Nearest Neighbor (k-NN), J48, and random forest (RF)] the proposed CCFSRFG-1 outperforms an existing solution (a CC-based FS, called CCEAFS) and CCFSRFG-2, and also when using all features in terms of accuracy, sensitivity, and specificity

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well-studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark and show its nonseparable nature irrespective of its number of peaks. We then propose a composite moving peaks benchmark suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of largescale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decompositionbased coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower dimensional components. A novel aspect of the framework is its efficient bilevel resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200 dimensions, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well-studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark and show its nonseparable nature irrespective of its number of peaks. We then propose a composite moving peaks benchmark suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of large-scale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decomposition-based coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower dimensional components. A novel aspect of the framework is its efficient bi-level resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200 dimensions, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems

3D-in-2D Displays for ATC.

This paper reports on the efforts and accomplishments of the 3D-in-2D Displays for ATC project at the end of Year 1. We describe the invention of 10 novel 3D/2D visualisations that were mostly implemented in the Augmented Reality ARToolkit. These prototype implementations of visualisation and interaction elements can be viewed on the accompanying video. We have identified six candidate design concepts which we will further research and develop. These designs correspond with the early feasibility studies stage of maturity as defined by the NASA Technology Readiness Level framework. We developed the Combination Display Framework from a review of the literature, and used it for analysing display designs in terms of display technique used and how they are combined. The insights we gained from this framework then guided our inventions and the human-centered innovation process we use to iteratively invent. Our designs are based on an understanding of user work practices. We also developed a simple ATC simulator that we used for rapid experimentation and evaluation of design ideas. We expect that if this project continues, the effort in Year 2 and 3 will be focus on maturing the concepts and employment in a operational laboratory settings

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Analysis of Airspace Traffic Structure and Air Traffic Control Techniques

Air traffic controller cognitive processes are a limiting factor in providing safe and efficient flow of traffic. Therefore, there has been work in understanding the factors that drive controllers decision-making processes. Prior work has identified that the airspace structure, defined by the reference elements, procedural elements and pattern elements of the traffic, is important for abstraction and management of the traffic. This work explores in more detail this relationship between airspace structure and air traffic controller management techniques. This work looks at the current National Airspace System (NAS) and identifies different types of high altitude sectors, based on metrics that are likely to correlate with tasks that controllers have to perform. Variations of structural patterns, such as flows and critical points were also observed. These patterns were then related to groupings by origins and destinations of the traffic. Deeper pilot-controller voice communication analysis indicated that groupings by flight plan received consistent and repeatable sequences of commands, which were identified as techniques. These repeated modifications generated patterns in the traffic, which were naturally associated with the standard flight plan groupings and their techniques. The identified relationship between flight plan groupings and management techniques helps to validate the grouping structure-base abstraction introduced by Histon and Hansman (2008). This motivates the adoption of a grouping-focused analysis of traffic structures on the investigation of how new technologies, procedures and concepts of operations will impact the way controllers manage the traffic. Consideration of such mutual effects between structure and controllers' cognitive processes should provide a better foundation for training and for engineering decisions that include a human-centered perspective.This work was financially supported by FAA grant 06-G-006 and NASA Cooperative Agreement NN06CN23A. Anton Koros and Eddie Sierra were the technical sponsors and provided valuable feedback and assistance

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin

An agent-based model for the sustainable management of navigation activities in the Saint Lawrence Estuary

Natural resource managers of protected areas are concerned with the management of human activities potentially harmful to ecosystems’ health and/or integrity. These systems where human interact with natural resources are called social-ecological systems (SES) and possess the characteristics of complex adaptive systems (e.g. co-evolution). The SES of navigation activities and whales interacting within the Saguenay–St. Lawrence Marine Park (SSLMP) and the projected St. Lawrence Estuary Marine Protected Area in Quebec, Canada, has been investigated and modelled using the agent-based modelling (ABM) technology: The resulting Marine Mammal and Maritime Traffic Simulator (3MTSim) is designed to support marine protected area managers in their effort to reduce the frequency and intensity of boat-whale co-occurrences within the St Lawrence Estuary and mitigate the risks of vessel strikes. This dissertation presents the building process of the 3MTSim’s boat ABM. The knowledge extracted from analyses of gathered and collected data relative to all forms of sailing and motorized navigation supported the decision to first focus on the modelling of commercial excursions (including whale-watching trips), cargo ships, and cruise liners. Data analyses allowed, for the first time, to draw a comprehensive portrait of navigation activities throughout the region where whales congregate in great numbers during the summer season. Among others, a quantitative analysis led to an accurate estimate of the total navigation time within each separate ecosystem of the region. This study identified areas intensively used by maritime traffic such as the mouth of the Saguenay River and offshore Les Escoumins. Several field campaigns carried out in the context of this project allowed to link some undesirable collective patterns of whale-watching excursions (regarding both whale conservation and SSLMP visitors’ experience) with contextual factors including whale species’ abundance and distribution, management gaps, and companies and captains’ decisions. The bounded rationality framework was chosen to investigate captains’ decision making and more generally the dynamics of the whole whale-watching SES. A portrait of the decision strategies followed by whale-watching captains has been drawn. The results will lead to a set of recommendations regarding the sustainable management of whale-watching excursions in and around the SSLMP. Results from field investigations and data analyses have fed the model building process, including an explicit representation of the whale-watching captains’ decision making. Data analyses revealed that cargo ships and ocean liners tend to follow predictable routes with low variability. Consequently, a complex behavioural modelling approach was deemed unnecessary in favour of a statistical approach, justified by the large volume of high-quality historical data available for both components. The pattern-oriented modelling approach proved appropriate for selecting a valid model of whale-watching excursions. Model simulations confirmed that whale-watching captains do favour the observation of a few rare rorqual species (e.g. humpback whales), leaving aside the most abundant one, namely the minke whales. Therefore, 3MTSim was run to quantify the impact that whale-watching captains changing their decision strategy could have on both whale exposure to boats (conservation concern) and excursion content (commercial concern). It was found that captains willing to avoid crowded observation sites and/or seeking to increase the diversity of species observed could have statistically significant benefits regarding conservation issues without affecting important features of their excursions. Finally, the convincing performance of the 3MTSim’s boat ABM ensures its safe use as a decision-support tool for management insofar as model limitations are understood and accounted for in the results and discussion