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Front Cover

Nov. 2017

Presents the front cover for this issue of the publication.



IEEE World Congress on Computational Intelligence WCCI 2018

Nov. 2017

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.



Table of Contents

Nov. 2017

Presents the table of contents for this issue of the publication.






CIM Editorial Board

Nov. 2017

Presents a listing of the Editorial Board for this issue of the publication.



End of Second Term as Editor-in-Chief [Editor's Remarks]

Nov. 2017

Presents the introductory editorial for this issue of the publication.



Staff List

Nov. 2017

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.



CIS Publications in the Spotlight [President's Message]

Nov. 2017

Presents the President’s message for this issue of the publication.



Conference Report on 2017 IEEE Congress on Evolutionary Computation (IEEE CEC 2017) [Conference Reports]

Nov. 2017

Presents information on the 2017 IEEE Congress on Evolutionary Computation.



Conference Report on 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017) [Conference Reports]

Nov. 2017

Presents information on the 2017 IEEE International Conference on Fuzzy Systems.



CIS Publication Spotlight [Publication Spotlight]

Nov. 2017

Presents a listing of books recently published in the field of computational intelligence.



Computational Intelligence in Aerospace Science and Engineering [Guest Editorial]

Nov. 2017

The articles in this special section focus on the use of computational intelligence in the aerospace industry. In an expanding world with limited resources, Computational Intelligence has become a necessity to handle the complexity of systems and processes. The aerospace sector, in particular, has stringent performance requirements on highly complex systems for which solutions are expected to be optimal and reliable at the same time. Computational intelligence techniques have been widely used to find solutions to global single or multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities.



Robust Design of a Supersonic Natural Laminar Flow Wing-Body

Nov. 2017

The robust design of a natural laminar flow wingbody for a supersonic business jet is here described. The pursued goal is to obtain a wing shape whose performance is influenced as least as possible by geometrical uncertainties. The starting point is a supersonic business jet wing-body that was already optimized for natural laminar flow using a deterministic objective function formulation. The definition of the optimization goal is based on special risk functions, namely Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), that are widely used in financial engineering community and that offer interesting advantages with respect to more classical approaches based on expectation or variance risk functions. VaR and CVaR are used in conjunction with two different stochastic optimization algorithms, namely the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the Surrogate-based Local Optimization (SBLO). These risk functions are computed using a very coarse sample set and their confidence intervals are computed using the bootstrap computational statistics technique. The results illustrate the feasibility of such a robust optimization approach for the application to industrial class robust design optimization problems in aerospace.



Modelling Behaviour in UAV Operations Using Higher Order Double Chain Markov Models

Nov. 2017

Creating behavioural models of human operators engaged in supervisory control tasks with UAVs is of great value due to the high cost of operator failures. Recent works in the field advocate the use of Hidden Markov Models (HMMs) and derivatives to model the operator behaviour, since they offer interpretable patterns for a domain expert and, at the same time, provide valuable predictions which can be used to detect abnormal behaviour in time. However, the first order Markov assumption in which HMMs rely, and the assumed independence between the operator actions along time, limit their modelling capabilities. In this work, we extend the study of behavioural modelling in UAV operations by using Double Chain Markov Models (DCMMs), which provide a flexible modelling framework in which two higher order Markov Chains (one hidden and one visible) are combined. This work is focused on the development of a process flow to rank and select DCMMs based on a set of evaluation measures that quantify the predictability and interpretability of the models. To evaluate and demonstrate the possibilities of this modelling strategy over the classical HMMs, the proposed process has been applied in a multi-UAV simulation environment.



An Intelligent Packing Programming for Space Station Extravehicular Missions

Nov. 2017

Packing programming for extravehicular missions to the space station is the process of arranging a set of missions into multiple extravehicular activities. It is an interesting combinatorial optimization problem developed from the traditional bin-packing problem. This paper first formulates a practical mathematical model that considers both the constraints of the time window for each extravehicular mission and the spacewalk time per astronaut. An Ant Colony Optimization (ACO) algorithm with a self-adaptation strategy and a new pheromone matrix characterizing the relationship between any two extravehicular missions is then proposed. The simulation results on various independent experiments show that the proposed ACO algorithm is capable of producing optimal packing programming schemes with a success rate of over 90%, which is acceptable for application to real-world problems.



Knowledge Transfer Through Machine Learning in Aircraft Design

Nov. 2017

The modern aircraft has evolved to become an important part of our society. Its design is multidisciplinary in nature and is characterized by complex analyses of mutually interdependent disciplines and large search spaces. Machine learning has, historically, played a significant role in aircraft design, primarily by approximating expensive physics-based numerical simulations. In this work, we summarize the current role of machine learning in this application domain, and highlight the opportunity of incorporating recent advances in the field to further its impact. Specifically, regression models (or surrogate models) that represent a major portion of the current efforts are generally built from scratch assuming a zero prior knowledge state, only relying on data from the ongoing target problem of interest. However, due to the incremental nature of design processes, there likely exists relevant knowledge from various related sources that can potentially be leveraged. As such, we present three relatively advanced machine learning technologies that facilitate automatic knowledge transfer in order to improve design performance. Subsequently, we demonstrate the efficacy of one of these technologies, i.e. transfer learning, on two use cases of aircraft engine design yielding noteworthy results. Our aim is to unveil this new application as a well-suited arena for the salient features of knowledge transfer in machine learning to come to the fore, thereby encouraging future research efforts.



Benchmarking Ensemble Classifiers with Novel Co-Trained Kernel Ridge Regression and Random Vector Functional Link Ensembles [Research Frontier]

Nov. 2017

Studies in machine learning have shown promising classification performance of ensemble methods employing "perturb and combine" strategies. In particular, the classical random forest algorithm performs the best among 179 classifiers on 121 UCI datasets from different domains. Motivated by this observation, we extend our previous work on oblique decision tree ensemble. We also propose an efficient co-trained kernel ridge regression method. In addition, a random vector functional link network ensemble is also introduced. Our experiments show that our two oblique decision tree ensemble variants and the co-trained kernel ridge regression ensemble are the top three ranked methods among the 183 classifiers. The proposed random vector functional link network ensemble also outperforms all neural network based methods used in the experiments.



PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]

Nov. 2017

Over the last three decades, a large number of evolutionary algorithms have been developed for solving multi-objective optimization problems. However, there lacks an upto-date and comprehensive software platform for researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve their real-world problems. The demand of such a common tool becomes even more urgent, when the source code of many proposed algorithms has not been made publicly available. To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files. More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators. Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.



How to Read Many-Objective Solution Sets in Parallel Coordinates [Educational Forum]

Nov. 2017

Rapid development of evolutionary algor ithms in handling many-objective optimization problems requires viable methods of visualizing a high-dimensional solution set. The parallel coordinates plot which scales well to high-dimensional data is such a method, and has been frequently used in evolutionary many-objective optimization. However, the parallel coordinates plot is not as straightforward as the classic scatter plot to present the information contained in a solution set. In this paper, we make some observations of the parallel coordinates plot, in terms of comparing the quality of solution sets, understanding the shape and distribution of a solution set, and reflecting the relation between objectives. We hope that these observations could provide some guidelines as to the proper use of the parallel coordinates plot in evolutionary manyobjective optimization.



A Primer on Cluster Analysis: 4 Basic Methods That (Usually) Work [Book Review]

Nov. 2017

This book examine the concept of clustering. Clustering was important in the past, has become more important in the present era of internet, social networks, and big data, and will continue to remain so in future. We need clustering to find subgroups of cancers, clusters of stars in our galaxies, to answer web queries, to understand group dynamics in social networks—the list goes on. The book develops the necessary concepts such as similarity, distance, clusters, computer view point, human view point, and cluster validation in a very logical and lucid manner with plenty of easy-to-follow examples and creative pictures. Although the book primarily focuses on four types of popular clustering algorithms, it provides adequate materials and pointers for interested readers to sail through a much wider family of clustering algorithms. This book will be very useful (and of course enjoyable to read) to a wide spectrum of readers including beginners, researchers, and practitioners. It consists of 11 chapters divided into two parts: Part I: The Art and Science of Clustering, which has five chapters and Part II: Four Basic Models & Algorithms that contains the remaining six chapters.



2019 IEEE International Conference on Fuzzy Systems

Nov. 2017

Presents information on the upcoming 2019 IEEE International Conference on Fuzzy Systems.



Conference Calendar [Conference Calendar]

Nov. 2017

Presents the CIS upcoming calendar of events and meetings.



Call for Papers for Journal Special Issues

Nov. 2017

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.



IEEE Conference on Computational Intelligence and Games

Nov. 2017

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.



2018 IEEE Symposium Series on Computational Intelligence

Nov. 2017

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.