Algorithm Design: A Fairness-Accuracy Frontier

We consider algorithmic design, enhancement, and improvement of evolutionary computation (EC) as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in EC can manipulate the parameter settings and operations of an EC algorithm by satisfying their own preferences rather than by following a fixed algorithm rule. EC algorithm designers or EC self-adaptive methods should construct appropriate rules and mechanisms for all agents (individuals) to conduct their evolution behavior correctly in order to definitely achieve the desired and pre-set objective(s) definitively. We propose a formal framework on parameter setting, strategy selection, and algorithmic design of EC by considering the strategy equilibrium implementation of a mechanism design problem in the search process. We attempt to use Nash strategy equilibrium (NE) concept in an implementation of an algorithmic mechanism design problem, but our proposed framework is not limited to Nash strategy equilibrium. The evaluation results present the efficiency of the framework. Its primary principle can be implemented in any EC algorithm that needs to consider the strategy selection issue in its optimization process. The final objective of our work is to implement EC design as an algorithmic mechanism design problem and establish EC fundamental aspects based on this perspective.

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.

The use of matrix visualization in algorithmic design

Nowadays, with the recent advances of wireless underwater communication and acoustic sensor devices technology, we are witnessing a surge in the exploration and exploitation of the ocean’s abundant natural resources. Accordingly, to fulfill the requirements of the exploration of the ocean, researchers have focused their work toward the design of methods and algorithms for the underwater acoustic sensor networks (UASNs). Although considerable research effort has been devoted to the development of a variety of UASN-based applications, very limited work has addressed the algorithmic design and analysis for UASN. To this end, we propose to provide a comprehensive design, development, and analysis of algorithms and protocols for UASNs. We discuss each of the fundamental UASN building blocks, such as (i) underwater acoustic communication channel modeling, (ii) sustainable coverage and target detection, (iii) Medium Access Control (MAC-layer design and time synchronization, (iv) localization algorithms design, and (v) underwater routing protocol. Then, we illustrate the different protocols from each category and compare their benefits and drawbacks. Finally, we discuss a few potential directions for future research related to the design of future generations of UASNs.

The application of population-based optimization algorithms in design is heavily driven by the translation and analysis of various data sets that represent a design problem; in evolutionary-based algorithms, these data sets are illustrated through two primary data streams: genes and fitness functions. The latter is frequently examined when analyzing the algorithm’s output, and the former is comparatively less so. This paper examines the role of genomic analysis in applying multi-objective evolutionary algorithms (MOEA) in design. The results demonstrate the significance of utilizing the genetic analysis to understand better the relationships between parameters used in the design problem’s formulation and differentiate between morphological differences in the algorithmic output not commonly observed through fitness-based analyses.

Roberto Baldacci (“ An Exact Algorithm for the Pickup and Delivery Problem with Time Windows ”) is a researcher in operations research at the Department of Electronics, Computer Science, and Systems (DEIS) of the University of Bologna, Italy. His major research interests are in the areas of transportation planning, logistics and distribution, and the solution of vehicle routing and scheduling problems over street networks. His research activities are in the theory and applications of mathematical programming including the design of new heuristic and exact methods for solving routing and location problems. Enrico Bartolini (“ An Exact Algorithm for the Pickup and Delivery Problem with Time Windows ”) holds a postdoctoral position at the University of Bologna. His research activity concerns the study and development of heuristic and exact algorithms for solving combinatorial optimization problems with applications in logistics and distribution systems, in particular network design problems and some generalizations of the vehicle routing problem. Saif Benjaafar (“ Optimal Control of an Assembly System with Multiple Stages and Multiple Demand Classes ”) is professor of industrial and systems engineering at the University of Minnesota, where he is also founding and current director of the Industrial & Systems Engineering Program, director of the Center for Supply Chain Research, and a faculty scholar with the Center for Transportation Studies. He was a Distinguished Senior Visiting Scientist at Honeywell Laboratories and a visiting professor at universities in France, Belgium, Hong Kong, China, and Singapore. His research is in the areas of supply chain management, service and manufacturing operations, and production and inventory systems, with a current focus on sustainability and environmental modeling. He serves on the editorial board of several journals including Manufacturing & Service Operations Management, Production and Operations Management, Naval Research Logistics, and IIE Transactions. He is a Fellow of the Institute of Industrial Engineers (IIE). Dimitris Bertsimas (“ Performance Analysis of Queueing Networks via Robust Optimization ”) is the Boeing Professor of Operations Research and codirector of the Operations Research Center at the Massachusetts Institute of Technology. This research is part of his work in the last decade on robust optimization for optimization and performance analysis of stochastic systems. Atul Bhandari (“ Revenue Management with Bargaining ”) is manager of the Algorithms Team at SmartOps. He supervises the design and development of enterprise inventory optimization algorithms, supervises modeling and analysis support for sales and implementation efforts, and leads educational sessions. He earned a Ph.D. in operations research from the Carnegie Mellon University Tepper School of Business. Sushil Bikhchandani (“ An Ascending Vickrey Auction for Selling Bases of a Matroid ”) is professor of decisions, operations, and technology management at the Anderson School of Management at the University of California, Los Angeles. He is interested in the economics of incentives and its application to auctions, market institutions, and social learning. J. Paul Brooks (“ Support Vector Machines with the Ramp Loss and the Hard Margin Loss ”) is an assistant professor of operations research in the Department of Statistical Sciences and Operations Research and a fellow of the Center for Study of Biological Complexity, Virginia Commonwealth University. He is currently secretary/treasurer of the INFORMS Section on Data Mining. His research interests include the design of optimization-based algorithms for data mining and their application to biomedical data. He is also interested in applications of optimization to models of cellular metabolism and network design problems. Sungyong Choi (“ A Multiproduct Risk-Averse Newsvendor with Law-Invariant Coherent Measures of Risk ”) is an instructor in the Department of Management Science and Information Systems at Rutgers University. Dr. Choi's research interests are in the area of stochastic modeling and its application in supply chain management. Milind Dawande (“ Production Planning with Patterns: A Problem from Processed Food Manufacturing ” and “ Quantifying the Impact of Layout on Productivity: An Analysis from Robotic-Cell Manufacturing ”) is professor and area coordinator of operations management at the School of Management, University of Texas at Dallas. His research interests are in discrete optimization problems in manufacturing and operations. His papers have appeared in a number of research outlets, including Operations Research, Management Science, Manufacturing & Service Operations Management, and the INFORMS Journal on Computing. Mehmet Demirci (“ Production Planning with Patterns: A Problem from Processed Food Manufacturing ”) is a supply chain sales engineer at SmartOps. He holds a Ph.D. degree in industrial engineering from the University of Pittsburgh. His research interests include inventory optimization, operations management, large-scale combinatorial optimization, and operations research applications in health care. Sven de Vries (“ An Ascending Vickrey Auction for Selling Bases of a Matroid ”) is a professor of operations research in the Department of Mathematics at the Universität Trier. His research interests include combinatorial optimization and auctions. Xiaowei Ding (“ A Top-Down Approach to Multiname Credit ”) is an associate at Morgan Stanley's Commodity Trading Group. Mohsen ElHafsi (“ Optimal Control of an Assembly System with Multiple Stages and Multiple Demand Classes ”) is a professor at the Anderson Graduate School of Management at the University of California, Riverside, where he also serves as associate dean and graduate advisor. He holds Ph.D. and M.Sc. degrees from the Industrial and Systems Engineering Department at the University of Florida and was the Honor Graduate. He received the Qualified Engineer degree, with honors, from the Ecole Nationale d'Ingénieurs de Tunis, Tunisia. His area of research includes operations and supply chain management, manufacturing and service operations, and production and inventory systems. Amr Farahat (“ A Comparison of Bertrand and Cournot Profits in Oligopolies with Differentiated Products ”) is an assistant professor at the Johnson Graduate School of Management at Cornell University. He obtained his doctoral degree in operations research from the Massachusetts Institute of Technology. His current research focuses on differentiated product pricing, inventory management, and competition. He is interested in problems at the interface of operations management, economics, and marketing. Vivek F. Farias (“ The Irrevocable Multiarmed Bandit Problem ”) is the Robert N. Noyce Career Development Assistant Professor of Management at the Sloan School of Management and the Operations Research Center at the Massachusetts Institute of Technology. His research focuses on revenue management, dynamic optimization, and the analysis of complex stochastic systems. The paper in this issue is part of the author's research in the context of dynamic optimization. David Gamarnik (“ Performance Analysis of Queueing Networks via Robust Optimization ”) is an associate professor of operations research at the Sloan School of Management of the Massachusetts Institute of Technology. His research interests include applied probability and stochastic processes, theory of random combinatorial structures and algorithms, and various applications. He currently serves as an associate editor of Annals of Applied Probability, Operations Research, Mathematics of Operations Research, and queueing systems journals. Srinagesh Gavirneni (“ Production Planning with Patterns: A Problem from Processed Food Manufacturing ”) is an assistant professor of operations management in the Johnson Graduate School of Management at Cornell University. His research interests are in the areas of supply chain management, inventory control, production scheduling, simulation, and optimization. His papers have appeared in Management Science, Manufacturing & Service Operations Management, Operations Research, European Journal of Operational Research, Operations Research Letters, IIE Transactions, and Interfaces. Previously he was an assistant professor in the Kelley School of Business at Indiana University, the chief algorithm design engineer of SmartOps, a software architect at Maxager Technology Inc., and a research scientist with Schlumberger. His undergraduate degree from IIT-Madras is in mechanical engineering, and he received an M.Sc. from Iowa State University and a Ph.D. from Carnegie Mellon University. Kay Giesecke (“ A Top-Down Approach to Multiname Credit ”) is assistant professor of management science and engineering at Stanford University. His research and teaching interests are in financial engineering. Lisa R. Goldberg (“ A Top-Down Approach to Multiname Credit ”) is executive director of analytic initiatives at MSCI Barra with responsibility for developing and prototyping financial risk and valuation models. Randolph W. Hall (“ Discounted Robust Stochastic Games and an Application to Queueing Control ”) is vice president of research, and professor of industrial and systems engineering, at the University of Southern California. After receiving a Ph.D. in civil engineering from the University of California, Berkeley, he has held research and faculty positions at General Motors, the University of California, Berkeley, and the University of Southern California, including dir

System-Level Design Environments (SLDEs) are often utilized for tackling the design complexity of modern embedded systems. SLDEs typically start with a specification capturing core algorithms. Algorithm development itself largely occurs in Algorithm Design Environments (ADE) with little or no hardware concern. Currently, algorithm and system design environments are disjoint; system level specifications are manually implemented which leads to the specification gap.In this paper, we bridge algorithm and system design environments creating a unified design flow facilitating algorithm and system co-design. It enables algorithm realizations over heterogeneous platforms, while still tuning the algorithm according to platform needs. Our design flow starts with algorithm design in Simulink, out of which a System Level Design Language (SLDL)-based specification is synthesized. This specification then is used for design space exploration across heterogeneous target platforms and abstraction levels, and, after identifying a suitable platform, synthesized to HW/SW implementations. It realizes a unified development cycle across algorithm modeling and system-level design with quick responses to design decisions on algorithm-, specification- and system exploration level. It empowers the designer to combine analysis results across environments, apply cross layer optimizations, which will yield an overall optimized design through rapid design iterations.We demonstrate the benefits on a MJPEG video encoder case study, showing early computation/communication estimation and rapid prototyping from Simulink models. Results from Virtual Platform performance analysis enable the algorithm designer to improve model structure to better match the heterogeneous platform in an efficient and fast design cycle. Through applying our unified design flow, an improved HW/SW is found yielding 50% performance gain compared to a pure software solution.KeywordsDesign IterationDesign Space ExplorationHeterogeneous PlatformJoint AlgorithmPlatform ExplorationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Introduction: There is a great potential that the quantum computing can change the way of algorithms and software development more than classical computers. Thus, this article will try to focus on how algorithm design and software development can be affected by quantum computing as well as what possibilities could appear when quantum principles are implemented into traditional paradigms. This paper aims at identifying the impact of quantum computing on algorithm and software advancement, through a discussion of essential quantum algorithms, quantum languages, as well as the opportunities and challenges of quantum technologies. Method: An extensive literature review and theoretical investigation was also performed to investigate the foundational concepts of quantum computing and subsequent effects on algorithm and software engineering. Some of the research questions included exploring the contrast between classical and quantum algorithms, reviewing current literature on quantum programming languages, and delving into examples of real-life deployments of quantum algorithms cross numerous domains. Results: This paper shows that quantum computing brings qualitatively new paradigms in the algorithm design and function while the quantum algorithms such as Shor’s and Grover’s perform exponentially faster certain problems. Software development for quantum has brought the need to devise new frameworks of coding in light of probability in quantum circuit. It is also comforting to note that there is still effort being made although in its most embryonic form to create quantum programming languages like Qiskit and Cirq. Some of challenges include quantum decoherence; limited number of quantum hardware; and need for strong error correction processes.Conclusion: While there are currently relatively few quantum algorithms it is believed that the findings in this field have the ability to revolutionize algorithm and software design and subjects like cryptography, optimization and AI. However, trends in quantum computing show that the constraints to computational capabilities are likely to be lifted to allow creativity to develop the most powerful software solutions

A design and dispatch optimization algorithm based on mixed integer linear programming for rural electrification

In the past, the speed of computers was mainly increased by increasing the speed of their logic element. Thus, the memory cycle time has increased by two orders of magnitude. Improvements in technology achieved in the last 20 years have increased the speed of processors by as much as three orders. Today, since the physical barrier of the speed of transfer of an electric signal has been reached, it is possible to achieve additional speed only by improving the computer organization or by using it more effectively. Current technology has made it possible for the processors to be combined into large parallel structures, and by a suitable organization of n processors it is possible to reach an n-fold increase in the rate of computation. Parallelism in computation has brought with it new problems both in the creation of new algorithms and programs, and in the design of computer architectures. Parallel algorithms and programs are closely connected with the architecture of parallel computers, and therefore design and analysis of parallel algorithms and programs cannot be considered independently of their implementation and the architecture of the computer on which they are to be implemented. Several examples are known from the history of parallel data processing, where a valuable concept in the design of algorithms, programs or computers has had a large impact on the efficiency of computation.

Exploiting variability in the design of genetic algorithms to generate telerehabilitation activities

This paper deals with the question of the influence of the design of cryptographic algorithm on their side channel vulnerability. On the example of the DPA attack by Kocher the complexity of the attack and their dependency on algorithmic parameters is analyzed. It is shown that the careful selection or design of algorithms can increase the security of implementations of algorithms in addition to and independent from conventional DPA attack countermeasures.

To improve the care for cancer patients, personalized treatment including monitoring and managing Quality of life (QoL) data collection of patients in his/her home environment, its integration and its analysis is necessary. Advanced technologies have been used to develop smartphone devices to support cancer patients and clinicians by integrating all patient-relevant data, helping with Patient Reported Outcomes (PRO), side effect management, appointments, and nutritional support. In this review the role and challenges of using smartphone applications for precision oncology is discussed. The methodology section includes the data collection, data integration and predictive modelling approaches. The design, development and evaluation of (AI/ML) algorithms of these apps need intended purpose of these algorithms, description of used mepthods, validity and appropriateness of the datasets, design of the algorithms, evaluation, implementation of these (AI/ML) algorithms and post treatment monitoring. Though Artificial intelligence (AI) based results showed higher diagnostic classification accuracy in most of the results, the advancement of these mobile apps technologies has a few limitations. ML techniques and DL are used to discover novel biomarkers for early detection and diagnostics, and AI are used to accelerate drug discovery, exploit biomarkers to accurately match patients to clinical trials, and personalize cancer therapy based only on patient's own data. AI based smartphone apps cannot be treated as autonomous rather used as an integrative tool for patient-relevant data, PRO, side effect management, appointments, nutritional support, emotional and social support, severity of pain detection and correct diagnosis at higher level. It should encourage the clinicians and care givers to support and establish patient-physician relationships with the help of these apps.

The paper mainly explains a fast detection algorithm of sine wave phase difference applied to embedded equipment and applies the algorithm to practical engineering. It samples and compares the data in the actual environment, gives the deviation between the calculation result of the algorithm and the theoretical value under different conditions, different parameters, and different sampling rates, and analyzes and compares the calculation results. The data of the experimental results provide a basis for the subsequent algorithm research. The design of the algorithm provides a new solution for the calculation of the phase difference algorithm in the field of communication.

This paper presents the steps followed in the design of hybrid stochastic local search algorithms for biobjective permutation flow shop scheduling problems. In particular, this paper tackles the three pairwise combinations of the objectives (i) makespan, (ii) the sum of the completion times of the jobs, and (iii) the weighted total tardiness of all jobs. The proposed algorithms are combinations of two local search methods: two-phase local search and Pareto local search. The design of the algorithms is based on a careful experimental analysis of crucial algorithmic components of the two search methods. The final results show that the newly developed algorithms reach very high performance: The solutions obtained frequently improve upon the best nondominated solutions previously known, while requiring much shorter computation times.KeywordsSchedule ProblemLocal SearchPareto FrontNondominated SolutionBiobjective ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.