http://www.bibsonomy.org/burst/concept/tag/algorithm,BibSonomy BuRST Feed for /concept/tag/algorithm,2008-08-21T12:55:57+02:00http://www.bibsonomy.org/bibtex/2cc34fc2f76d00e1368ac7e6bcd4904d5/jacquenotjacquenot2008-06-26T18:58:40+02:00holland genetic, algorithm, <span style="color:#555555;">John H. <a href="http://www.bibsonomy.org/author/Holland">Holland</a> </span><em>University of Michigan Press, </em>(<em>1975</em>)Thu Jun 26 18:58:40 CEST 2008{Unknown Binding}Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence1975holland genetic, algorithm, {John Holland's Adaptation in Natural and Artificial Systems is one of the classics in the field of complex adaptive systems. Holland is known as the father of genetic algorithms and classifier systems and in this tome he describes the theory behind these algorithms. Drawing on ideas from the fields of biology and economics, he shows how computer programs can evolve. The book contains mathematical proofs that are accessible only to those with strong backgrounds in engineering or science.} {Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications. In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics. Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements. John H. Holland is Professor of Psychology and Professor of Electrical Engineering and Computer Science at the University of Michigan. He is also Maxwell Professor at the Santa Fe Institute and is Director of the University of Michigan/Santa Fe Institute Advanced Research Program.}http://www.bibsonomy.org/bibtex/2c639f9022c6bd28d716c62d28ac6691e/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00algorithm, MasterMind Evolutionary <span style="color:#555555;">J. J. <a href="http://www.bibsonomy.org/author/Merelo-Guervos">Merelo-Guervos</a> and P. <a href="http://www.bibsonomy.org/author/Castillo">Castillo</a> and V. M. <a href="http://www.bibsonomy.org/author/Rivas">Rivas</a> </span><em>Applied Soft Computing</em><em>6(2):170--179</em><em>January2006. </em>Thu Jun 19 17:46:40 CEST 2008Applied Soft ComputingJanuary2170--179Finding a needle in a haystack using hints and evolutionary computation: the case of evolutionary MasterMind62006algorithm, MasterMind Evolutionary In this paper we present a new version of an evolutionary algorithm that finds the hidden combination in the game of MasterMind by using hints on how close is a combination played to it. The evolutionary algorithm finds the hidden combination in an optimal number of guesses, is efficient in terms of memory and CPU, and examines only a minimal part of the search space. The algorithm is fast, and indeed previous versions can be played in real time on the world wide web. This new version of the algorithm is presented and compared with theoretical bounds and other algorithms. We also examine how the algorithm scales with search space size, and its performance for different values of the EA parameters.http://www.bibsonomy.org/bibtex/2a30ea546032308429d3ff3ef5a848e44/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00online selection algorithms, programming, classification, scheme, (artificial c-class Classification, classifier, evolutionary extraction, genetic intelligence), algorithm, problem, learning design pattern ranking feature classifier <span style="color:#555555;">Durga Prasad <a href="http://www.bibsonomy.org/author/Muni">Muni</a> and Nikhil R. <a href="http://www.bibsonomy.org/author/Pal">Pal</a> and Jyotirmoy <a href="http://www.bibsonomy.org/author/Das">Das</a> </span><em>IEEE Transactions on Systems, Man and Cybernetics, Part B</em><em>36(1):106--117</em><em>February2006. </em>Thu Jun 19 17:46:40 CEST 2008IEEE Transactions on Systems, Man and Cybernetics, Part BFebruary1106--117Genetic programming for simultaneous feature selection and classifier design362006online selection algorithms, programming, classification, scheme, (artificial c-class Classification, classifier, evolutionary extraction, genetic intelligence), algorithm, problem, learning design pattern ranking feature classifier This paper presents an online feature selection algorithm using genetic programming (GP). The proposed GP methodology simultaneously selects a good subset of features and constructs a classifier using the selected features. For a c-class problem, it provides a classifier having c trees. In this context, we introduce two new crossover operations to suit the feature selection process. As a byproduct, our algorithm produces a feature ranking scheme. We tested our method on several data sets having dimensions varying from 4 to 7129. We compared the performance of our method with results available in the literature and found that the proposed method produces consistently good results. To demonstrate the robustness of the scheme, we studied its effectiveness on data sets with known (synthetically added) redundant/bad features.http://www.bibsonomy.org/bibtex/21b6d44558a487ed2af8a0e483758f5da/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Genetic Dynamic Demes Parallel Algorithm, <span style="color:#555555;">Mariusz <a href="http://www.bibsonomy.org/author/Nowostawski">Nowostawski</a> and Riccardo <a href="http://www.bibsonomy.org/author/Poli">Poli</a> </span>(<em>1999</em>)Thu Jun 19 17:46:40 CEST 2008Dynamic Demes Parallel Genetic Algorithm1999Genetic Dynamic Demes Parallel Algorithm, Dynamic Demes is a new method for the parallelisation of evolutionary algorithms. It was derived as a combination of two other parallelisation algorithms: the master-slave distributed fitness evaluation model and the static subpopulation model. In this paper we present the algorithm, perform a theoretical analysis of its performance and present experimental results where we compared Dynamic Demes with other algorithms.http://www.bibsonomy.org/bibtex/2e2d554623d1517590a072c7417199911/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00programming: trail, genetic algorithm, programming, evolution, algorithms, verification Fe migrating Poster, Santa analytic measurement, self-organising differential symbolic regression, <span style="color:#555555;">Zuzana <a href="http://www.bibsonomy.org/author/Oplatkova">Oplatkova</a> and Ivan <a href="http://www.bibsonomy.org/author/Zelinka">Zelinka</a> </span><em>GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, </em><em>1, </em><em>page949--950. </em><em>Seattle, Washington, USA, </em><em>ACM Press, </em><em>8-12 July2006. </em>Thu Jun 19 17:46:40 CEST 2008Seattle, Washington, USA{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation8-12 July949--950Investigation on artificial ant using analytic programming12006programming: trail, genetic algorithm, programming, evolution, algorithms, verification Fe migrating Poster, Santa analytic measurement, self-organising differential symbolic regression, http://www.bibsonomy.org/bibtex/2387e39e186dfdd975392f6d88eefd69f/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00model algorithms, Bayesian building Efficiency HBOA, Hierarchical Estimation Sporadic distribution algorithm, genetic optimisation enhancement, of BOA, <span style="color:#555555;">Martin <a href="http://www.bibsonomy.org/author/Pelikan">Pelikan</a> and Kumara <a href="http://www.bibsonomy.org/author/Sastry">Sastry</a> and David E. <a href="http://www.bibsonomy.org/author/Goldberg">Goldberg</a> </span><em>Genetic Programming and Evolvable Machines</em><em>9(1):53--84</em><em>March2008. </em>Thu Jun 19 17:46:40 CEST 2008Genetic Programming and Evolvable MachinesMarch153--84Sporadic model building for efficiency enhancement of the hierarchical {BOA}92008model algorithms, Bayesian building Efficiency HBOA, Hierarchical Estimation Sporadic distribution algorithm, genetic optimisation enhancement, of BOA, Efficiency enhancement techniques such as parallelisation and hybridisation are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and analyses sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimisation algorithm (hBOA) and other estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once in every few iterations (generations), whereas in the remaining iterations, only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup, which decreases the asymptotic time complexity of model building in hBOA by a factor of $$\Uptheta(n^{0.26})$$ to $$\Uptheta(n^{0.5}),$$ where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, if model building is the bottleneck, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building. The paper also presents a dimensional model to provide a heuristic for scaling the structure-building period, which is the only parameter of the proposed sporadic model-building approach. The paper then tests the proposed method and the rule for setting the structure-building period on the problem of finding ground states of 2D and 3D Ising spin glasses.http://www.bibsonomy.org/bibtex/27f8a3f967a63031241546d6af058a011/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00programming, distributions, masks homologous crossover crossover, variable-length recombination algorithm, algorithms, genetic schema theory, <span style="color:#555555;">Riccardo <a href="http://www.bibsonomy.org/author/Poli">Poli</a> and Nicholas Freitag <a href="http://www.bibsonomy.org/author/McPhee">McPhee</a> </span><em>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), </em><em>page104--111. </em><em>San Francisco, California, USA, </em><em>Morgan Kaufmann, </em><em>7-11 July2001. </em>Thu Jun 19 17:46:40 CEST 2008San Francisco, California, USAProceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)7-11 July104--111Exact Schema Theory for {GP} and Variable-length {GA}s with Homologous Crossover2001programming, distributions, masks homologous crossover crossover, variable-length recombination algorithm, algorithms, genetic schema theory, http://www.bibsonomy.org/bibtex/2dd49611540843e43ce5cf6b93ea9c6f7/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00operators, computation, classification, C4.5 genetic classification algorithm, problems, mathematical data operators search decision systems, BGP trees, hybrid pattern blocks, databases, mining, evolutionary algorithms, induction CN2 programming, building <span style="color:#555555;">S. E. <a href="http://www.bibsonomy.org/author/Rouwhorst">Rouwhorst</a> and A. P. <a href="http://www.bibsonomy.org/author/Engelbrecht">Engelbrecht</a> </span><em>Proceedings of the 2000 Congress on Evolutionary Computation CEC00, </em><em>1, </em><em>page633--638. </em><em>La Jolla Marriott Hotel La Jolla, California, USA, </em><em>IEEE Press, </em><em>6-9 July2000. </em>Thu Jun 19 17:46:40 CEST 2008La Jolla Marriott Hotel La Jolla, California, USAProceedings of the 2000 Congress on Evolutionary Computation CEC006-9 July633--638Searching the Forest: Using Decision Trees as Building Blocks for Evolutionary Search in Classification Databases12000operators, computation, classification, C4.5 genetic classification algorithm, problems, mathematical data operators search decision systems, BGP trees, hybrid pattern blocks, databases, mining, evolutionary algorithms, induction CN2 programming, building A new evolutionary search algorithm, called BGP (Building-block approach to Genetic Programming), to be used for classification tasks in data mining, is introduced. It is different from existing evolutionary techniques in that it does not use indirect representations of a solution, such as bit strings or grammars. The algorithm uses decision trees of various sizes as individuals in the populations and operators, e.g. crossover, are performed directly on the trees. When compared to the C4.5 and CN2 induction algorithms on a benchmark set of problems, BGP shows very good resultshttp://www.bibsonomy.org/bibtex/2d8d47f1e41fe7958071bc42494919a75/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00genetic algorithms, hardware hardware, evolvable evolutionary fast evaluation, algorithm, fitness <span style="color:#555555;">Mehrdad <a href="http://www.bibsonomy.org/author/Salami">Salami</a> and Tim <a href="http://www.bibsonomy.org/author/Hendtlass">Hendtlass</a> </span><em>Genetic Programming and Evolvable Machines</em><em>6(2):139--162</em><em>June2005. </em>Thu Jun 19 17:46:40 CEST 2008Genetic Programming and Evolvable MachinesJune2139--162The Fast Evaluation Strategy for Evolvable Hardware62005genetic algorithms, hardware hardware, evolvable evolutionary fast evaluation, algorithm, fitness An evolutionary algorithm implemented in hardware is expected to operate much faster than the equivalent software implementation. However, this may not be true for slow fitness evaluation applications. This paper introduces a fast evolutionary algorithm (FEA) that does not evaluate all new individuals, thus operating faster for slow fitness evaluation applications. Results of a hardware implementation of this algorithm are presented that show the real time advantages of such systems for slow fitness evaluation applications. Results are presented for six optimisation functions and for image compression hardware.http://www.bibsonomy.org/bibtex/2a96f7c3d42103ab94b13badef5d869f0/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00genetic meta, evolution, neuronal programming learning, bucket brigade, nets, algorithm, SALM, associative genetical fractals PSALM, algorithms, self-reference, introsepection, EURISKO, <span style="color:#555555;">Jurgen <a href="http://www.bibsonomy.org/author/Schmidhuber">Schmidhuber</a> </span><em>14 May1987. </em>Thu Jun 19 17:46:40 CEST 200814 MayEvolutionary Principles in Self-Referential Learning. On Learning now to Learn: The Meta-Meta-Meta...-HookDiploma Thesis1987genetic meta, evolution, neuronal programming learning, bucket brigade, nets, algorithm, SALM, associative genetical fractals PSALM, algorithms, self-reference, introsepection, EURISKO, http://www.bibsonomy.org/bibtex/2397b02ffa8dadb2d0b3f0117b383317f/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00algorithms, alignment, sequences sequence programming, alignments, genetic multiple DNA algorithm, progressive <span style="color:#555555;">Conrad <a href="http://www.bibsonomy.org/author/Shyu">Shyu</a> and Luke <a href="http://www.bibsonomy.org/author/Sheneman">Sheneman</a> and James A. <a href="http://www.bibsonomy.org/author/Foster">Foster</a> </span><em>Genetic Programming and Evolvable Machines</em><em>5(2):121--144</em><em>June2004. </em>Thu Jun 19 17:46:40 CEST 2008Genetic Programming and Evolvable MachinesJune2121--144Multiple Sequence Alignment with Evolutionary Computation52004algorithms, alignment, sequences sequence programming, alignments, genetic multiple DNA algorithm, progressive we provide a brief review of current work in the area of multiple sequence alignment (MSA) for DNA and protein sequences using evolutionary computation (EC). We detail the strengths and weaknesses of EC techniques for MSA. In addition, we present two novel approaches for inferring MSA using genetic algorithms. Our first approach uses a GA to evolve an optimal guide tree in a progressive alignment algorithm and serves as an alternative to the more traditional heuristic techniques such as neighbor-joining. The second novel approach facilitates the optimization of a consensus sequence with a GA using a vertically scalable encoding scheme in which the number of iterations needed to find the optimal solution is approximately the same regardless the number of sequences being aligned. We compare both of our novel approaches to the popular progressive alignment program Clustal W. Experiments have confirmed that EC constitutes an attractive and promising alternative to traditional heuristic algorithms for MSA.http://www.bibsonomy.org/bibtex/2356d0e4d678fc2e87c26914b73107f1d/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00evolutionary black-box, genetic algorithm, analysis, runtime algorithms <span style="color:#555555;">Tobias <a href="http://www.bibsonomy.org/author/Storch">Storch</a> </span><em>Genetic Programming and Evolvable Machines</em><em>7(2):171--193</em><em>August2006. </em><em>Special Issue: Best of GECCO 2005 . </em>Thu Jun 19 17:46:40 CEST 2008Genetic Programming and Evolvable MachinesAugustSpecial Issue: Best of GECCO 20052171--193On the impact of objective function transformations on evolutionary and black-box algorithms72006evolutionary black-box, genetic algorithm, analysis, runtime algorithms Different objective functions characterise different problems. However, certain fitness transformations can lead to easier problems although they are still a model of the considered problem. In this article, the class of not worsening transformations for a simple population-based evolutionary algorithm (EA) is described completely. That is the class of functions that transfers easy problems in easy ones and difficult problems in difficult ones. Surprisingly, this class $$\mathcal{T}_{{\rm rank}}$$ for the rank-based EA equals that for all black-box algorithms. The importance of the black-box algorithms' knowledge of the transformation is also pointed out. Hence, a comparison with the class $$\mathcal{T}_{{\rm prop}}$$ of not worsening transformations for a similar EA which applies fitness-proportional selection, shows that $$\mathcal{T}_{{\rm rank}}$$ is a proper superset of $$\mathcal{T}_{{\rm prop}}$$. Moreover, $$\mathcal{T}_{{\rm rank}}$$ is a proper subset of the corresponding class for random search. Finally, the minimal and maximal classes of not worsening transformations are described completely, too.http://www.bibsonomy.org/bibtex/2797af734fa2ebc20d8ad574c5a7eeddd/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00fingerprint Bayes classification, identification, visual primitive methods, processing classifier, (artificial programming, learning discovery, algorithms, operator database, algorithm, feature-learning classification composite extraction, feature image method, genetic NIST-4 Bayesian databases intelligence), operations <span style="color:#555555;">Xuejun <a href="http://www.bibsonomy.org/author/Tan">Tan</a> and B. <a href="http://www.bibsonomy.org/author/Bhanu">Bhanu</a> and Yingqiang <a href="http://www.bibsonomy.org/author/Lin">Lin</a> </span><em>IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews</em><em>35(3):287--300</em>(<em>Aug</em>)Thu Jun 19 17:46:40 CEST 2008IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews3287--300Fingerprint classification based on learned features35Augfingerprint Bayes classification, identification, visual primitive methods, processing classifier, (artificial programming, learning discovery, algorithms, operator database, algorithm, feature-learning classification composite extraction, feature image method, genetic NIST-4 Bayesian databases intelligence), operations In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3percent and 91.6percent, respectively, which compare favourably with other published research and are one of the best results published to date.http://www.bibsonomy.org/bibtex/2a1ca23cd9f8f366d205e0f86bf1b347a/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00application algorithms, real service, genetic provider shop evolutionary applications, world job algorithm, programming, scheduling, <span style="color:#555555;">Ivan T. <a href="http://www.bibsonomy.org/author/Tanev">Tanev</a> and Takashi <a href="http://www.bibsonomy.org/author/Uozumi">Uozumi</a> and Yoshiharu <a href="http://www.bibsonomy.org/author/Morotome">Morotome</a> </span><em>GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, </em><em>page1219--1226. </em><em>New York, </em><em>Morgan Kaufmann Publishers, </em><em>9-13 July2002. </em>Thu Jun 19 17:46:40 CEST 2008New YorkGECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference9-13 July1219--1226An Application Service Provider Approach For Hybrid Evolutionary Algorithm-based Real-world Flexible Job Shop Scheduling Problem2002application algorithms, real service, genetic provider shop evolutionary applications, world job algorithm, programming, scheduling, scheduling of customers' orders in factories of plastic injection machines (FPIM) as a case of real-world flexible job shop scheduling problem (FJSS). The objective of discussed work is to provide FPIM with high business speed which implies (a) providing a customers with convenient way for remote online access to the factory's database and (b) developing an efficient scheduling routine for planning the assignment of the submitted customers' orders to FPIM machines. Remote online access to FPIM database, approached via delivering the software as a Web-service in accordance with the application service provider (ASP) paradigm is proposed. As an approach addressing the issue of efficient scheduling routine a hybrid evolutionary algorithm (HEA) combining priority-dispatching rules (PDRs) with GA, is developed. An implementation of HEA as a database stored procedure is discussed. Performance evaluation results are presented. The results obtained for evolving a schedule of 400 customers' orders on experimental model of FPIM indicate that the business delays in order of half an hour can be achieved.http://www.bibsonomy.org/bibtex/286912c6feeb51dc1cc890d133faf8db7/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Gene high data expression energy evolutionary algorithm, analysis computing, programming, analysis, algorithms, instrumentation gene Programming, physics genetic Expression <span style="color:#555555;">Liliana <a href="http://www.bibsonomy.org/author/Teodorescu">Teodorescu</a> </span><em>IEEE Nuclear Science Symposium Conference Record, </em><em>1, </em><em>page143--147. </em><em>IEEE, </em><em>23-29 October2005. </em>Thu Jun 19 17:46:40 CEST 2008IEEE Nuclear Science Symposium Conference Record23-29 October143--147High energy physics data analysis with gene expression programming12005Gene high data expression energy evolutionary algorithm, analysis computing, programming, analysis, algorithms, instrumentation gene Programming, physics genetic Expression Gene expression programming is a new evolutionary algorithm that overcomes many limitations of the more established genetic algorithms and genetic programming. Its first application to high energy physics data analysis is presented. The algorithm was successfully used for event selection on samples with both low and high background level. The signal/background classification accuracy was over 90percent in all cases.http://www.bibsonomy.org/bibtex/2832dd0162458163f07313f825e330edb/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Graph-based Local Fixation, model, drift, selection Spatial algorithms, Genetic Moran algorithm, process, genetic evolutionary <span style="color:#555555;">P. A. <a href="http://www.bibsonomy.org/author/Whigham">Whigham</a> and Grant <a href="http://www.bibsonomy.org/author/Dick">Dick</a> </span><em>Genetic Programming and Evolvable Machines</em><em>9(2):157--170</em><em>June2008. </em><em>Special Issue on Theoretical foundations of evolutionary computation . </em>Thu Jun 19 17:46:40 CEST 2008Genetic Programming and Evolvable MachinesJuneSpecial Issue on Theoretical foundations of evolutionary computation2157--170Evolutionary dynamics for the spatial Moran process92008Graph-based Local Fixation, model, drift, selection Spatial algorithms, Genetic Moran algorithm, process, genetic evolutionary Evolutionary dynamics for the Moran process have been previously examined within the context of fixation behaviour for introduced mutants, where it was demonstrated that certain spatial structures act as amplifiers of selection. This article will revisit the assumptions for this spatial Moran process and show that proportional global fitness, introduced as part of the Moran process, is necessary for the amplification of selection to occur. Here it is shown that under the condition of local proportional fitness selection the amplification property no longer holds. In addition, regular structures are also shown to have a modified fixation probability from a panmictic population when local selection is applied. Theoretical results from population genetics, which suggest fixation probabilities are independent of geography, are discussed in relation to these local graph-based models and shown to have different assumptions and therefore not to be in conflict with the presented results. This paper examines the issue of fixation probability of an introduced advantageous allele in terms of spatial structure and various spatial parent selection models. The results describe the relationship between structured populations and individual selective advantage in a problem independent manner. This is of significant interest to the theory of fine-grained spatially-structured evolutionary algorithms since the interaction of selection and space for diversity maintenance, selection strength and convergence underlies resulting evolutionary trajectories.http://www.bibsonomy.org/bibtex/283af1b75214e6ceed267362f7c2a10d9/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00profiling genetic cancer, microarray biomarkers, prostate evolutionary algorithms, Molecular algorithm, programming, diagnostics, <span style="color:#555555;">Jianjun <a href="http://www.bibsonomy.org/author/Yu">Yu</a> and Jindan <a href="http://www.bibsonomy.org/author/Yu">Yu</a> and Arpit A. <a href="http://www.bibsonomy.org/author/Almal">Almal</a> and Saravana M. <a href="http://www.bibsonomy.org/author/Dhanasekaran">Dhanasekaran</a> and Debashis <a href="http://www.bibsonomy.org/author/Ghosh">Ghosh</a> and William P. <a href="http://www.bibsonomy.org/author/Worzel">Worzel</a> and Arul M. <a href="http://www.bibsonomy.org/author/Chinnaiyan">Chinnaiyan</a> </span><em>Neoplasia</em><em>9(4):292--303</em><em>April2007. </em>Thu Jun 19 17:46:40 CEST 2008NeoplasiaApril4292--303Feature Selection and Molecular Classification of Cancer Using Genetic Programming92007profiling genetic cancer, microarray biomarkers, prostate evolutionary algorithms, Molecular algorithm, programming, diagnostics, Despite important advances in microarray-based molecular classification of tumours, its application in clinical settings remains formidable. This is in part due to the limitation of current analysis programs in discovering robust biomarkers and developing classifiers with a practical set of genes. Genetic programming (GP) is a type of machine learning technique that uses evolutionary algorithm to simulate natural selection as well as population dynamics, hence leading to simple and comprehensible classifiers. Here we applied GP to cancer expression profiling data to select feature genes and build molecular classifiers by mathematical integration of these genes. Analysis of thousands of GP classifiers generated for a prostate cancer data set revealed repetitive use of a set of highly discriminative feature genes, many of which are known to be disease associated. GP classifiers often comprise five or less genes and successfully predict cancer types and subtypes. More importantly, GP classifiers generated in one study are able to predict samples from an independent study, which may have used different microarray platforms. In addition, GP yielded classification accuracy better than or similar to conventional classification methods. Furthermore, the mathematical expression of GP classifiers provides insights into relationships between classifier genes. Taken together, our results demonstrate that GP may be valuable for generating effective classifiers containing a practical set of genes for diagnostic/prognostic cancer classification.http://www.bibsonomy.org/bibtex/2ec68ec7db4d6594c6b5fa87f39e70db5/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00Multi-objective circuits, genetic algorithms, hardware, Knowledge Adaptive Evolutionary algorithm, design evolvable discovery of <span style="color:#555555;">Shuguang <a href="http://www.bibsonomy.org/author/Zhao">Zhao</a> and Licheng <a href="http://www.bibsonomy.org/author/Jiao">Jiao</a> </span><em>Genetic Programming and Evolvable Machines</em><em>7(3):195--210</em><em>October2006. </em>Thu Jun 19 17:46:40 CEST 2008Genetic Programming and Evolvable MachinesOctober3195--210Multi-objective evolutionary design and knowledge discovery of logic circuits based on an adaptive genetic algorithm72006Multi-objective circuits, genetic algorithms, hardware, Knowledge Adaptive Evolutionary algorithm, design evolvable discovery of Evolutionary design of circuits (EDC), an important branch of evolvable hardware which emphasises circuit design, is a promising way to realize automated design of electronic circuits. In order to improve evolutionary design of logic circuits in efficiency, scalability and capability of optimisation, a genetic algorithm based novel approach was developed. It employs a gate-level encoding scheme that allows flexible changes of functions and interconnections of logic cells comprised, and it adopts a multi-objective evaluation mechanism of fitness with weight-vector adaptation and circuit simulation. Besides, it features an adaptation strategy that enables crossover probability and mutation probability to vary with individuals' diversity and genetic-search process. It was validated by the experiments on arithmetic circuits especially digital multipliers, from which a few functionally correct circuits with novel structures, less gate count and higher operating speed were obtained. Some of the evolved circuits are the most efficient or largest ones (in terms of gate count or problem scale) as far as we know. Moreover, some novel and general principles have been discerned from the EDC results, which are easy to verify but difficult to dig out by human experts with existing knowledge. These results argue that the approach is promising and worthy of further research.http://www.bibsonomy.org/bibtex/275ac880b20d5949158d04fed3b75a6c9/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00robotics, algorithm, programming, genetic flying algorithms, evolutionary <span style="color:#555555;">Peter <a href="http://www.bibsonomy.org/author/Augustsson">Augustsson</a> and Krister <a href="http://www.bibsonomy.org/author/Wolff">Wolff</a> and Peter <a href="http://www.bibsonomy.org/author/Nordin">Nordin</a> </span><em>GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, </em><em>page1279--1285. </em><em>New York, </em><em>Morgan Kaufmann Publishers, </em><em>9-13 July2002. </em>Thu Jun 19 17:35:00 CEST 2008New YorkGECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference9-13 July1279--1285Creation Of {A} Learning, Flying Robot By Means Of Evolution2002robotics, algorithm, programming, genetic flying algorithms, evolutionary We demonstrate the first instance of a real on-line robot learning to develop feasible flying (flapping) behavior, using evolution. Here we present the experiments and results of the first use of evolutionary methods for a flying robot. With nature's own method, evolution, we address the highly non-linear fluid dynamics of flying. The flying robot is constrained in a test bench where timing and movement of wing flapping is evolved to give maximal lifting force. The robot is assembled with standard off-the-shelf R/C servomotors as actuators. The implementation is a conventional steady-state linear evolutionary algorithm.http://www.bibsonomy.org/bibtex/2a947ae1451e7d05721aa01db592e4d85/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00Problem, Complexity, programming, genetic Development, Scaling Evolutionary Algorithm, algorithms, Heterochrony <span style="color:#555555;">Wolfgang <a href="http://www.bibsonomy.org/author/Banzhaf">Banzhaf</a> and Julian <a href="http://www.bibsonomy.org/author/Miller">Miller</a> </span><em>Frontiers of Evolutionary Computation, </em><em>volume11ofGenetic Algorithms And Evolutionary Computation Series, </em><em>chapter 11, </em><em>Kluwer Academic Publishers, </em><em>Boston, MA, USA, </em>(<em>2004</em>)Thu Jun 19 17:35:00 CEST 2008Boston, MA, USAFrontiers of Evolutionary Computation1173--99Genetic Algorithms And Evolutionary Computation SeriesThe Challenge of Complexity112004Problem, Complexity, programming, genetic Development, Scaling Evolutionary Algorithm, algorithms, Heterochrony the challenge provided by the problem of evolving large amounts of computer code via Genetic Programming. We argue that the problem is analogous to what Nature had to face when moving to multi-cellular life. We propose to look at developmental processes and there mechanisms to come up with solutions for this ``challenge of complexity'' in Genetic Programming