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318
An Overview of Evolutionary Algorithms: Practical Issues and Common Pitfalls
 Information and Software Technology
, 2001
"... An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and realvalued representations are discussed for parameter optimi ..."
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Cited by 57 (0 self)
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An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and realvalued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algorithms is discussed.
The Island Model Genetic Algorithm: On Separability, Population Size and Convergence
 Journal of Computing and Information Technology
, 1998
"... Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic di ..."
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Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. It is also possible that since linearly separable problems are often used to test Genetic Algorithms, that Island Models may simply be particularly well suited to exploiting the separable nature of the test problems. We explore this possibility by using the infinite population models of simple genetic algorithms to study how Island Models can track multiple search trajectories. We also introduce a simple model for better understanding when Island Model genetic algorithms may have an advantage when processing some test problems. We provide empirical results for both linearly separa...
General Schema Theory for Genetic Programming with SubtreeSwapping Crossover
 In Genetic Programming, Proceedings of EuroGP 2001, LNCS
, 2001
"... In this paper a new, general and exact schema theory for genetic programming is presented. The theory includes a microscopic schema theorem applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. A more macroscopic schema ..."
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Cited by 53 (30 self)
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In this paper a new, general and exact schema theory for genetic programming is presented. The theory includes a microscopic schema theorem applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. A more macroscopic schema theorem is also provided which is valid for crossover operators in which the probability of selecting any two crossover points in the parents depends only on their size and shape. The theory is based on the notions of Cartesian node reference systems and variablearity hyperschemata both introduced here for the first time. In the paper we provide examples which show how the theory can be specialised to specific crossover operators and how it can be used to derive an exact definition of effective fitness and a sizeevolution equation for GP. 1
Comparison of linear, nonlinear, and feature selection methods for EEG signal classification
 IEEE Trans. Neural Syst. Rehabil. Eng
"... Abstract—The reliable operation of brain–computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need ..."
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Cited by 48 (1 self)
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Abstract—The reliable operation of brain–computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The highdimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement. Index Terms—Brain–computer interface (BCI) , electroencephalogram (EEG), feature selection, genetic algorithms (GA), neural networks, pattern classification, support vector machines (SVM). I.
Multiuser MIMOOFDM for NextGeneration Wireless Systems
, 2007
"... This overview portrays the 40year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multipleinput multipleoutput (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highl ..."
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Cited by 44 (5 self)
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This overview portrays the 40year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multipleinput multipleoutput (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the socalled rankdeficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their stateoftheart solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMOOFDM systems and characterizes their achievable performance. A further section aims for identifying novel cuttingedge genetic algorithm (GA)aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GAassisted optimization techniques, which were recently proposed also
Topological Interpretation of Crossover
 IN PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE
, 2004
"... In this paper we give a representationindependent topological definition of crossover that links it tightly to the notion of fitness landscape. Building around this definition, a geometric/topological framework for evolutionary algorithms is introduced that clarifies the connection between repre ..."
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Cited by 40 (27 self)
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In this paper we give a representationindependent topological definition of crossover that links it tightly to the notion of fitness landscape. Building around this definition, a geometric/topological framework for evolutionary algorithms is introduced that clarifies the connection between representation, genetic operators, neighbourhood structure and distance in the landscape. Traditional genetic operators for binary strings are shown to fit the framework. The advantages of this interpretation are discussed
A Schema Theorem for ContextFree Grammars
 In 1995 IEEE Conference on Evolutionary Computation
, 1995
"... The basic Schema Theorem for genetic algorithms is modified for a grammaticallybased learning system. A contextfree grammar is used to define a language in which each sentence is mapped to a fitness value. The derivation trees associated with these sentences are used to define the structure of sch ..."
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The basic Schema Theorem for genetic algorithms is modified for a grammaticallybased learning system. A contextfree grammar is used to define a language in which each sentence is mapped to a fitness value. The derivation trees associated with these sentences are used to define the structure of schemata. The effect of crossover and mutation on schemata is described. A schema theorem is developed which describes how sentences of a language are propagated during evolution. 1. Introduction The Schema Theorem for genetic algorithms (GA) [3] defines how useful structures in a population of strings are propagated during the evolution of a solution. It gives a lower bound on the propagation of schemata from one generation to the next. A GA uses a fitness function to evaluate the population, and the genetic operations of crossover and mutation to search for new strings. The fixedlength nature of GA's has been extended with genetic programming (GP) [4], which uses a treebased representati...
Exact Schema Theory for Genetic Programming and Variablelength Genetic Algorithms with OnePoint Crossover
, 2001
"... A few schema theorems for Genetic Programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rathe ..."
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Cited by 37 (16 self)
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A few schema theorems for Genetic Programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rather than an exact value. This paper presents theoretical results for GP with onepoint crossover which overcome this problem. Firstly, we give an exact formulation for the expected number of instances of a schema at the next generation in terms of microscopic quantities. Thanks to this formulation we are then able to provide an improved version of an earlier GP schema theorem in which some (but not all) schema creation events are accounted for. Then, we extend this result to obtain an exact formulation in terms of macroscopic quantities which makes all the mechanisms of schema creation explicit. This theorem allows the exact formulation of the notion of effective fitness in GP and opens the way to future work on GP convergence, population sizing, operator biases, and bloat, to mention only some of the possibilities.
A survey of sequential Monte Carlo methods for economics and finance
, 2009
"... This paper serves as an introduction and survey for economists to the field of sequential Monte Carlo methods which are also known as particle filters. Sequential Monte Carlo methods are simulation based algorithms used to compute the highdimensional and/or complex integrals that arise regularly in ..."
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Cited by 34 (7 self)
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This paper serves as an introduction and survey for economists to the field of sequential Monte Carlo methods which are also known as particle filters. Sequential Monte Carlo methods are simulation based algorithms used to compute the highdimensional and/or complex integrals that arise regularly in applied work. These methods are becoming increasingly popular in economics and finance; from dynamic stochastic general equilibrium models in macroeconomics to option pricing. The objective of this paper is to explain the basics of the methodology, provide references to the literature, and cover some of the theoretical results that justify the methods in practice.
Optimizationbased automated home video editing system
 IEEE Trans. on Circuit and Syst. for Video Tech
, 2004
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