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Particle swarm optimization
, 1995
"... eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications ..."
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Cited by 1658 (15 self)
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eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described, 1
Data Clustering: A Review
 ACM COMPUTING SURVEYS
, 1999
"... Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exp ..."
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Cited by 1300 (13 self)
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Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify crosscutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
Genetic Programming
, 1997
"... Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring ..."
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Cited by 869 (12 self)
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Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring genetic operations such as crossover (sexual recombination) and mutation. John Holland's pioneering Adaptation in Natural and Artificial Systems (1975) described how an analog of the evolutionary process can be applied to solving mathematical problems and engineering optimization problems using what is now called the genetic algorithm (GA). The genetic algorithm attempts to find a good (or best) solution to the problem by genetically breeding a population of individuals over a series of generations. In the genetic algorithm, each individual in the population represents a candidate solut
The particel swarm: Explosion, stability, and convergence in a multidimensional complex space [J
 IEEE Transactions on Evolutionary Compution
"... The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained ..."
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Cited by 447 (9 self)
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The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained how it works. Further, traditional versions of the algorithm have had some dynamical properties that were not considered to be desirable, notably the particles ’ velocities needed to be limited in order to control their trajectories. The present paper analyzes the particle’s trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A 5dimensional depiction is developed, which completely describes the system. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system’s convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the optimization power of the particle swarm
The Advantages of Evolutionary Computation
, 1997
"... Evolutionary computation is becoming common in the solution of difficult, realworld problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific ..."
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Cited by 398 (5 self)
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Evolutionary computation is becoming common in the solution of difficult, realworld problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific advantages include the flexibility of the procedures, as well as the ability to selfadapt the search for optimum solutions on the fly. As desktop computers increase in speed, the application of evolutionary algorithms will become routine. 1 Introduction Darwinian evolution is intrinsically a robust search and optimization mechanism. Evolved biota demonstrate optimized complex behavior at every level: the cell, the organ, the individual, and the population. The problems that biological species have solved are typified by chaos, chance, temporality, and nonlinear interactivities. These are also characteristics of problems that have proved to be especially intractable to classic methods of o...
Genetic Algorithms, Noise, and the Sizing of Populations
 COMPLEX SYSTEMS
, 1991
"... This paper considers the effect of stochasticity on the quality of convergence of genetic algorithms (GAs). In many problems, the variance of buildingblock fitness or socalled collateral noise is the major source of variance, and a populationsizing equation is derived to ensure that average sig ..."
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Cited by 240 (85 self)
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This paper considers the effect of stochasticity on the quality of convergence of genetic algorithms (GAs). In many problems, the variance of buildingblock fitness or socalled collateral noise is the major source of variance, and a populationsizing equation is derived to ensure that average signaltocollateralnoise ratios are favorable to the discrimination of the best building blocks required to solve a problem of bounded deception. The sizing relation is modified to permit the inclusion of other sources of stochasticity, such as the noise of selection, the noise of genetic operators, and the explicit noise or nondeterminism of the objective function. In a test suite of five functions, the sizing relation proves to be a conservative predictor of average correct convergence, as long as all major sources of noise are considered in the sizing calculation. These results suggest how the sizing equation may be viewed as a coarse delineation of a boundary between what a physicist might call two distinct phases of GA behavior. At low population sizes the GA makes many errors of decision, and the quality of convergence is largely left to the vagaries of chance or the serial fixup of flawed results through mutation or other serial injection of diversity. At large population sizes, GAs can reliably discriminate between good and bad building blocks, and parallel processing and recombination of building blocks lead to quick solution of even difficult deceptive problems. Additionally, the paper outlines a number of extensions to this work, including the development of more refined models of the relation between generational average error and ultimate convergence quality, the development of online methods for sizing populations via the estimation of populations...
Parameter control in evolutionary algorithms
 IEEE Transactions on Evolutionary Computation
"... Summary. The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classifica ..."
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Cited by 237 (30 self)
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Summary. The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classification of different approaches based on a number of complementary features, and pay special attention to setting parameters onthefly. This has the potential of adjusting the algorithm to the problem while solving the problem. This paper is intended to present a survey rather than a set of prescriptive details for implementing an EA for a particular type of problem. For this reason we have chosen to interleave a number of examples throughout the text. Thus we hope to both clarify the points we wish to raise as we present them, and also to give the reader a feel for some of the many possibilities available for controlling different parameters. 1
A Genetic Algorithm Tutorial
 Statistics and Computing
, 1994
"... This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic algorit ..."
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Cited by 234 (5 self)
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This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.
Strongly Typed Genetic Programming
 Evolutionary Computation
, 1994
"... Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection [Koza 92]. However, it has the limitation known as "closure", i.e. that all the variables, constants, arguments for functions, and values returned from functions must be of ..."
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Cited by 233 (1 self)
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Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection [Koza 92]. However, it has the limitation known as "closure", i.e. that all the variables, constants, arguments for functions, and values returned from functions must be of the same data type. To correct this deficiency, we introduce a variation of genetic programming called "strongly typed" genetic programming(STGP). In STGP, variables, constants, arguments, and returned values can be of any data type with the provision that the data type for each such value be specified beforehand. This allows the initialization process and the genetic operators to only generate syntactically correct parse trees. Key concepts for STGP are generic functions, which are not true strongly typed functions but rather templates for classes of such functions, and generic data types, which are analogous. To illustrate STGP, we present four examples involving vector/matrix manip...
The Reactive Tabu Search
, 1993
"... this paper the concept of chaotic attractor is used only as an example of a dynamic behavior that could a#ect the search process, we summarize the main characteristics and refer to [13] for a detailed theoretical analysis. Chaotic attractors are characterized by a "contraction of the areas", so that ..."
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Cited by 224 (25 self)
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this paper the concept of chaotic attractor is used only as an example of a dynamic behavior that could a#ect the search process, we summarize the main characteristics and refer to [13] for a detailed theoretical analysis. Chaotic attractors are characterized by a "contraction of the areas", so that trajectories starting with di#erent initial conditions will be compressed in a limited area of the configuration space, and by a "sensitive dependence upon the initial conditions", so that di#erent trajectories will diverge. For an analytical characterization of this sensitive dependence, it is convenient to introduce the concept of Lyapunov exponent. Let us consider the function g that maps the point at step n