Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

Multiple criteria decision making

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

In evolutionary multiobjective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms (EAs). In addition, it becomes increasingly important to incorporate user preferences because it will be less likely to achieve a representative subset of the Pareto-optimal solutions using a limited population size as the number of objectives increases. This paper proposes a reference vector-guided EA for many-objective optimization. The reference vectors can be used not only to decompose the original multiobjective optimization problem into a number of single-objective subproblems, but also to elucidate user preferences to target a preferred subset of the whole Pareto front (PF). In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space. An adaptation strategy is proposed to dynamically adjust the distribution of the reference vectors according to the scales of the objective functions. Our experimental results on a variety of benchmark test problems show that the proposed algorithm is highly competitive in comparison with five state-of-the-art EAs for many-objective optimization. In addition, we show that reference vectors are effective and cost-efficient for preference articulation, which is particularly desirable for many-objective optimization. Furthermore, a reference vector regeneration strategy is proposed for handling irregular PFs. Finally, the proposed algorithm is extended for solving constrained many-objective optimization problems.

A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

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.

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

In practical applications like power system, the distribution of the measurement noise is unknown or frequently deviates from the assumed Gaussian model, often being characterized by heavy tails and sometimes generating impulse noise, named outliers. Under these conditions, the performances of the conventional state estimation (SE) methods that assume known and Gaussian noise, will be greatly degraded. To solve these problems, this paper proposes a two-stage robust power system SE method. In the first stage, a GM-estimator that uses conventional SCADA measurements is proposed by using the robust scale estimation, projection statistics and Huber-type M-estimator. Its result is further combined with the PMU measurement to achieve a linear robust estimation in the second stage. The projection statistics is used to identify and downweight the bad leverage measurements. The GM-estimator bounds the influences of bad data and the estimation residuals, yielding good statistical efficiency for Gaussian noise or even other non-Gaussian heavy tailed distributions, like Laplace, Gaussian mixture, etc. Numerical tests on the IEEE-30 bus system under various cases verify the effectiveness and robustness of the proposed method.

A two-stage robust power system state estimation method with unknown measurement noise

A Membrane-Inspired Evolutionary Algorithm Based on Population P Systems and Differential Evolution for Multi-Objective Optimization

Worst-case execution time test generation can be hard if tested programs use complex heuristics. This is especially true in the case of the knapsack problem, which is often called “the easiest NP-complete problem”. For randomly generated test data, the expected running time of some algorithms for this problem is linear. We present an approach for generation of tests against algorithms for the knapsack problem. This approach is based on genetic algorithms. It is evaluated on five algorithms, including one simple branch-and-bound algorithm, two algorithms by David Pisinger and their partial implementations. The results show that the presented approach performs statistically better than generation of random tests belonging to certain classes. Moreover, a class of tests that are especially hard for one of the algorithms was discovered by the genetic algorithm.

Bio-Inspired Computing - Theories and Applications

In this paper, a quantum-inspired evolutionary algorithm based on P systems (QEPS) is used for radar emitter signals to promote the application of membrane computing. QEPS combines the framework and evolution rules of P systems with the chromosome representation and evolutionary mechanism of quantum-inspired evolutionary algorithms (QIEA). With good global search capability and rapid convergence, QEPS can extract specific information from radar emitter signals in a short span of time. Experiments are carried out on linear-frequency modulated radar emitter signals with 10 db signal-to-noise rate to test the effectiveness and practicality of the introduced approach. Experimental results show that QEPS performs better than the greedy algorithm and the counterpart QIEA.

A quantum-inspired evolutionary algorithm based on P systems for radar emitter signals

The main problem of time-frequency atom decomposition (TFAD) lies in an extremely high computational load. This paper pres- ents a fast implementation method based on quantum-inspired genetic algorithm (QGA). Instead of finding the optimal atom in greedy implementation algorithm, this method is to search a satisfactory atom in every iteration of TFAD. Making full use of QGA's advantages such as good global search capability, rapid convergence and short computing time, the method reduces greatly the computational load of TFAD. Experiments conducted on radar emitter signals verify the effectiveness and practicality of the introduced method.

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Gexiang Zhang

Papers

A two-stage robust power system state estimation method with unknown measurement noise

A Membrane-Inspired Evolutionary Algorithm Based on Population P Systems and Differential Evolution for Multi-Objective Optimization

Bio-Inspired Computing - Theories and Applications

A quantum-inspired evolutionary algorithm based on P systems for radar emitter signals

Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition