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

 Four appendices summarize valuable resources available for the reader: Appendix A contains printed and recorded resources, Appendix B suggests web-related resources, Appendix C discusses GP software tools, including Discipulus, the GP software developed by the authors, and Appendix D mentions events most closely related to the field of genetic programming. URLs can be found online at http://mkp.com/GPIntro.

Genetic Programming: An Introduction

A fuzzy system is implemented to dynamically adapt the inertia weight of the particle swarm optimization algorithm (PSO). Three benchmark functions with asymmetric initial range settings are selected as the test functions. The same fuzzy system has been applied to all three test functions with different dimensions. The experimental results illustrate that the fuzzy adaptive PSO is a promising optimization method, which is especially useful for optimization problems with a dynamic environment.

Fuzzy adaptive particle swarm optimization

Fuzzy Rule-Based Systems Evolutionary Computation Introduction to Genetic Fuzzy Systems Genetic Tuning Processes Learning with Genetic Algorithms Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach Genetic Fuzzy Rule-Based Systems Based on the lterative Rule Learning Approach Other Genetic Fuzzy Rule-Based System Other Kinds of Evolutionary Fuzzy Systems Applications.

Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases

This paper constitutes a first study of the Particle Swarm Optimization (PSO) method in Multiobjective Optimization (MO) problems. The ability of PSO to detect Pareto Optimal points and capture the shape of the Pareto Front is studied through experiments on well-known non-trivial test functions. The Weighted Aggregation technique with fixed or adaptive weights is considered. Furthermore, critical aspects of the VEGA approach for Multiobjective Optimization using Genetic Algorithms are adapted to the PSO framework in order to develop a multi-swarm PSO that can cope effectively with MO problems. Conclusions are derived and ideas for further research are proposed.

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Particle swarm optimization method in multiobjective problems

Evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionary algorithm are adapted via a fuzzy system. Benefits of the methodology are illustrated in the process of classifying the iris data set. Possible extensions of the methods are summarized.

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Implementation of evolutionary fuzzy systems

: Report developed under STTR contract for topic "Cognitive models for learning to control dynamic systems" demonstrated a swarm intelligence learning algorithm and its application in unmanned aerial vehicle (UAV) mission planning. A new UAV assignment model was developed that reduces the dimension of the solution space and is easily adapted by computational intelligence algorithms. A version of particle swarm optimization (PSO) was applied to accomplish the mission optimization. Numerical experimental results illustrate that it efficiently achieves the optima and demonstrates the effectiveness of combining the model and PSO to solve complex UAV assignment problems. The time to complete mission plans for operationally realistic scenarios is reduced by 3-4 orders of magnitude compared with the mixed-integer linear programming approach being used by AFRL at WPAFB. A computer game was also developed to investigate how humans interact with swarm intelligence. The game is based on an NK landscape. It is concluded that the combination of a human-swarm team may have advantages in certain environments, such as dynamic decision making tasks.

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Yaobin Chen

Papers

Implementation of evolutionary fuzzy systems

Cognitive Models for Learning to Control Dynamic Systems