AWEA2010VeeraEtAL techreport Evolutionary Approaches for Wind Resource Assessment Annual meeting of American Wind Energy Association, (WINDPOWER 2011) 2011 Technical Poster American Wind Energy Association 2011 mcdermott-etal-bcs-2011 inproceedings Creative Transformations: How Generative and Evolutionary Music can Inform Music {HCI} Newcastle, UK 2011 Proceedings of {BCS} {HCI} 2011 Workshop -- When Words Fail: What can Music Interaction tell us about {HCI}? British Computer Society 2011 NeumannOReillyWagner2011:GPTP inproceedings Genetic and Evolutionary Computation Genetic Programming: From Theory to Practice, GPTP 2011 Springer Computational Complexity Analysis of Genetic Programming - Initial Results and Future Directions, 7 Rick L. Riolo and Ekaterina Vladislavleva and Jason Moore Ann Arbor 2011 part of \cite{Riolo:2011:GPTP} 2011 NoelVeeraOReilly2011:GPTP inproceedings Genetic and Evolutionary Computation Genetic Programming: From Theory to Practice, GPTP 2011 Springer Baseline Genetic Programming Symbolic Regression on Benchmarks for Sensory Evaluation Modeling 10 Rick L. Riolo and Ekaterina Vladislavleva and Jason Moore Ann Arbor 2011 part of \cite{Riolo:2011:GPTP} 2011 mcdermott-oreilly-xg inproceedings Natalio Krosnogor and Pier-Luca Lanzi An Executable Graph Representation for Evolutionary Generative Music Dublin 2011 GECCO '11 ACM Press 2011 petabricksOfflineTuner inproceedings Natalio Krosnogor and Pier-Luca Lanzi An Efficient Evolutionary Algorithm for Solving Bottom Up Problems Dublin 2011 GECCO '11 ACM Press 2011 OReillyEtAl:HPEC09 inproceedings Multi-Objective Optimization of Sparse Array Computations 2009 Proceedings of Workshop on High Performance Embedded Computing, HPEC '09 2009 5465782 inproceedings POSTER: Performing network coding at network nodes with O/E/O wavelength conversion equipment incurs negligible additional cost. Our methodology finds minimum O/E/O equipment for multicast network coding on a minimal wavelength subgraph with one link failure reliability. Network coding in optical networks with O/E/O based wavelength conversion O/E/O wavelength conversion equipment;link failure reliability;minimal wavelength subgraph;multicast network coding;network node;optical network;multicast communication;network coding;optical communication equipment;optical fibre networks;optical wavelength conversion;telecommunication network reliability;wavelength division multiplexing; 1 -3 march 2010 Optical Fiber Communication (OFC), collocated National Fiber Optic Engineers Conference, 2010 Conference on (OFC/NFOEC) 2010-03 4753087 inproceedings We investigate the problem of integrating network coding into heterogeneous wireless networks where a number of coding nodes are to be placed among legacy nodes that do not handle network coding operations well. In particular, we seek to understand better the following questions: 1) how many coding nodes are needed, 2) where should the coding nodes be located, and 3) how should the coding nodes interact with other non-coding nodes? To this aim, we apply our previously proposed evolutionary algorithm that operates as a distributed protocol and present quantifiable results through various simulations. We also consider the algorithmpsilas operation in lossy environments and show that the temporally distributed structure of our algorithm offers a significant advantage in overcoming the adverse effect of packet erasures. Integrating network coding into heterogeneous wireless networks adverse effects;coding nodes;distributed protocol;distributed structure;evolutionary algorithm;heterogeneous wireless networks;legacy nodes;network coding integration;packet erasures;codes;evolutionary computation;protocols;radio networks; 1 -7 nov. 2008 Military Communications Conference, 2008. MILCOM 2008. IEEE 10.1109/MILCOM.2008.4753087 2008-11 4215813 inproceedings We wish to minimize the resources used for network coding while achieving the desired throughput in a multicast scenario. We employ evolutionary approaches, based on a genetic algorithm, that avoid the computational complexity that makes the problem NP-hard. Our experiments show great improvements over the sub-optimal solutions of prior methods. Our new algorithms improve over our previously proposed algorithm in three ways. First, whereas the previous algorithm can be applied only to acyclic networks, our new method works also with networks with cycles. Second, we enrich the set of components used in the genetic algorithm, which improves the performance. Third, we develop a novel distributed framework. Combining distributed random network coding with our distributed optimization yields a network coding protocol where the resources used for coding are optimized in the setup phase by running our evolutionary algorithm at each node of the network. We demonstrate the effectiveness of our approach by carrying out simulations on a number of different sets of network topologies. Evolutionary Approaches To Minimizing Network Coding Resources NP-hard problem;computational complexity;distributed optimization;distributed random network;evolutionary approaches;genetic algorithm;multicast scenario;network coding resources;computational complexity;encoding;genetic algorithms;telecommunication network topology; 0743-166X 1991 -1999 may 2007 INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE 10.1109/INFCOM.2007.231 2007-05 4755908 inproceedings In the past, knowledge processing (anomaly detection, target identification, social network analysis) of sensor data did not require real-time processing speeds. However, the rapid growth in the size of the data and the shortening time scale of the required data analysis are driving the need for applications that provide real-time signal and knowledge processing at the sensor front end. Many knowledge processing techniques, such as Bayesian networks, social networks, and neural networks, have a graph abstraction. Graph algorithms are difficult to parallelize and thus cannot take advantage of multi-core architectures. Many graph operations can be cast as sparse linear algebra operations. While this increases the ease of programming, parallel sparse algorithms are still inefficient. This paper presents a search-based mapping and routing approach for sparse operations. Since finding well-performing maps and routes for sparse operations is a computationally intensive task, the mapping and routing algorithms have been parallelized to take advantage of the Lincoln Laboratory cluster computing capability, LLGrid. Our parallelization of the approach yielded near linear speed up and the mapping and routing results demonstrate over an order of magnitude performance improvement over traditional mapping techniques. Performance Modeling and Mapping of Sparse Computations data analysis;graph algorithms;knowledge processing;performance mapping;performance modeling;search-based mapping;search-based routing;sparse computations;sparse linear algebra;data analysis;graph theory;linear algebra;signal processing; 448 -456 july 2008 DoD HPCMP Users Group Conference, 2008. DOD HPCMP UGC 10.1109/DoD.HPCMP.UGC.2008.66 2008-07 springerlink:10.1007/s10472-011-9229-y article We demonstrate a means of knowledge discovery through feature extraction that exploits the search history of a search-based optimization run. We regress a symbolic model ensemble from optimization run search points and their objective scores. The frequency of a variable in the models of the ensemble indicates to what the extent it is an influential feature. Our demonstration uses a genetic programming symbolic regression software package that is designed to be off-the-shelf . By default, the only parameter needed in order to evolve a suite of models is how long the user is willing to wait. Then the user can easily specify which models should go forward in terms of sufficient accuracy and complexity. For illustration purposes, we consider a sequencing heuristic used to chain remote sensors from one to the next: place the most reliable sensor last . The heuristic is derived based on the mathematical form of the optimization objective function which places emphasis on the decision variable pertaining to the last sensor. Feature extraction on optimized sensor sequences demonstrates that the heuristic is usually effective though it is not always trustworthy. This is consistent with knowledge in sensor processing. Annals of Mathematics and Artificial Intelligence Feature extraction from optimization samples via ensemble based symbolic regression 1012-2443 1-19 Computer Science 2011 CSAIL, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 10.1007/s10472-011-9229-y http://dx.doi.org/10.1007/s10472-011-9229-y Springer Netherlands 2011 springerlink:10.1007/978-3-642-20407-4_17 incollection The distance between pairs of individuals is a useful concept in the study of evolutionary algorithms. It is particularly useful to define a distance which is coherent with, i.e. related to, the action of a particular operator. We present the first formal, general definition of this operator-distance coherence. We also propose a new distance function, based on the multi-step transition probability (MSTP), that is coherent with any GP operator for which the one-step transition probability (1STP) between individuals can be defined. We give an algorithm for 1STP in the case of subtree mutation. Because MSTP is useful in GP investigations, but impractical to compute, we evaluate a variety of means to approximate it. We show that some syntactic distance measures give good approximations, and attempt to combine them to improve the approximation using a GP symbolic regression method. We conclude that 1STP itself is a sufficient indicator of MSTP for subtree mutation. Lecture Notes in Computer Science 190-202 Proceedings of European Conference on Genetic Programming, EuroGP-2011 http://dx.doi.org/10.1007/978-3-642-20407-4_17 Springer How Far Is It from Here to There? A Distance That Is Coherent with GP Operators Silva, Sara and Foster, James and Nicolau, Miguel and Machado, Penousal and Giacobini, Mario 6621 2011 EvoDesignOpt, CSAIL, MIT, USA 10.1007/978-3-642-20407-4_17 2011 springerlink:10.1007/978-3-642-13800-3_28 incollection We demonstrate a means of knowledge discovery through feature extraction that exploits the search history of an optimization run. We regress a symbolic model ensemble from optimization run search points and their objective scores. The frequency of a variable in the models of the ensemble indicates to what the extent it is an influential feature. Our demonstration uses a genetic programming symbolic regression software package that is designed to be off-the-shelf . By default, the only parameter needed in order to evolve a suite of models is how long the user is willing to wait. Then the user can easily specify which models should go forward in terms of sufficient accuracy and complexity. For illustration purposes, we consider a common design heuristic in serial sensor sequencing: place the most reliable sensor last . The heuristic is derived based on the mathematical form of the objective function that lays emphasis on the decision variable pertaining to the last sensor. Feature extraction on optimized sensor sequences indicates that the heuristic is usually effective though it is not always trustworthy. This is consistent with knowledge in sensor processing. Lecture Notes in Computer Science 251-265 Learning and Intelligent Optimization http://dx.doi.org/10.1007/978-3-642-13800-3_28 Springer Berlin / Heidelberg Feature Extraction from Optimization Data via DataModeler's Ensemble Symbolic Regression Blum, Christian and Battiti, Roberto 6073 2010 CSAIL, Massachusetts Institute of Technology Cambridge MA 02139 USA 10.1007/978-3-642-13800-3_28 2010 stephenson2003meta article ACM SIGPLAN Notices, PLDI 2003 Meta optimization: Improving compiler heuristics with machine learning 77--90 38 2003 5 ACM 2003 oreilly:1999:fogpepslm unpublished http://www.ai.mit.edu/people/unamay/popsize.ps Foundations of Genetic Programming: Effective Population Size, Linking and Mixing genetic algorithms genetic programming April 1999 Summary of recent work: \cite{goldberg:1998:good} \cite{oreilly:1998:edGPp} \cite{oreilly:1998:fssaGP} GECCO'99 workshop 2 pages 1999-04 o1995troubling article Foundations of genetic algorithms The troubling aspects of a building block hypothesis for genetic programming 73--88 3 1995 Morgan Kaufmann 1995 RePEc:wop:safiwp:94-02-001 techreport In this paper we rigorously formulate the Schema Theorem for Genetic Programming (GP). This involves defining a schema, schema order, and defining length and accounting for the variable length and the non-homologous nature of GP'S representation. The GP Schema Theorem and the related notion of a GP Building Block are used to construct a testable hypothetical account of how GP searches by hierarchically combining building blocks. Since building blocks need to have consistent above average fitness and compactness, and since the term in the GP Schema Theorem that expresses compactness is a random variable, the proposed account of GP search behavior is based on empirically questionable statistical assumptions. In particular, low variance in schema fitness is questionable because the performance of a schema depends in a highly sensitive manner on the context provided by the programs in which it is found. GP crossover is likely to change this context from one generation to the next which results in high variance in observed schema fitness. Low variance in compactness seems fortuitous rather than assured in GP because schema-containing programs change their sizes essentially at random. The Troubling Aspects of a Building Block Hypothesis for Genetic Programming February 94-02-001 1994 Working Papers http://ideas.repec.org/p/wop/safiwp/94-02-001.html Santa Fe Institute 1994-02 OReilly:thesis phdthesis An Analysis of Genetic Programming genetic algorithms genetic programming Carleton University Ottawa-Carleton Institute for Computer Science, Ottawa, Ontario, Canada 22 September 1995 http://www.santafe.edu/~unamay http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/oreilly/refs.ps.gz 224 pages 1995 o1994program article Parallel Problem Solving from Nature, PPSN III Program search with a hierarchical variable length representation: Genetic programming, simulated annealing and hill climbing 397--406 1994 Springer 1994 OReilly:1994:GPSAHCsfi techreport This paper presents a comparison of Genetic Programming(GP) with Simulated Annealing (SA) and Stochastic Iterated Hill Climbing (SIHC) based on a suite of program discovery problems which have been previously tackled only with GP. All three search algorithms employ the hierarchical variable length representation for programs brought into recent prominence with the GP paradigm. We feel it is not intuitively obvious that mutation-based adaptive search can handle program discovery yet, to date, for each GP problem we have tried, SA or SIHC also work. Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing genetic algorithms genetic programming 1399 Hyde Park Road Santa Fe, New Mexico 87501-8943 USA 94-04-021 1994 Hard copy sent from SFI by pdb@santafe.edu (Patricia Brunello) Second revision dated 6/6/94 This paper was co-authored with Franz Oppacher of Carleton University. An abridged version appears in the Proceedings of the Third Conference on Parallel Problem Solving from Nature, Springer Verlag, 1994. A longer version is SFI Technical Report 94-04-021 13 pages Santa Fe Institute 1994 brooks2004sensing article International Journal of Humanoid Robotics Sensing and manipulating built-for-human environments 1--28 1 2004 1 2004 poli:1998:evsn inproceedings This paper first analyses the impact of variance on schema transmission. Working from an exact derivation of the expected variance in schema transmission, it derives and analyses the signal-to-noise ratio for schemata. The paper then presents short term schema transmission probability results that focus on newly created schemata in the population. The analysis reveals the relative dependencies between schema transmission, population size, schema measured fitness, schema fragility and schema creation. genetic algorithms genetic programming 284--292 Genetic Programming 1998: Proceedings of the Third Annual Conference San Francisco, CA, USA http://cswww.essex.ac.uk/staff/poli/papers/Poli-GP1998-Schema.pdf 9 pages Morgan Kaufmann Analysis of Schema Variance and Short Term Extinction Likelihoods John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo University of Wisconsin, Madison, Wisconsin, USA 1-55860-548-7 22-25 July 1998 GP-98. Based on \cite{poli:1998:evsnTR} 1998 OReilly:1996:aigp2 incollection In order to analyze Genetic Programming (GP), this chapter compares it with two alternative adaptive search algorithms, Simulated Annealing (SA) and Stochastic Iterated Hill Climbing (SIHC). SIHC and SA are used to solve program discovery problems posed in the style of GP. In separate versions they employ either GP's crossover operator or a mutation operator. The comparisons in terms of likelihood of success and efficiency show them to be effective. Based upon their success, hybrid versions of GP and hill climbing are designed that improve upon a canonical version of GP. Program discovery practitioners may find it useful to coherently view all the algorithms this chapter considers by using the perspective of evolution. 2 Peter J. Angeline and K. E. {Kinnear, Jr.} A Comparative Analysis of {GP} genetic algorithms genetic programming Cambridge, MA, USA 0-262-01158-1 23--44 1996 Advances in Genetic Programming 2 MIT Press 1996 goldberg:1998:good inproceedings Using deliberately designed primitive sets, we investigate the relationship between context-based expression mechanisms and the size, height and density of genetic program trees during the evolutionary process. We show that contextual semantics influence the composition, location and flows of operative code in a program. In detail we analyze these dynamics and discuss the impact of our findings on micro-level descriptions of genetic programming. LNCS genetic algorithms genetic programming 16--36 Proceedings of the First European Workshop on Genetic Programming Berlin http://citeseer.ist.psu.edu/96596.html 21 pages Springer-Verlag http://www.ai.mit.edu/people/unamay/papers/eurogp.final.ps Where does the Good Stuff Go, and Why? How contextual semantics influence program structure in simple genetic programming Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer and Terence C. Fogarty Paris 3-540-64360-5 1391 14-15 April 1998 EuroGP'98 Also presented at the Canadian AI-98 Workshop on Evolutionary Computation Schedule, 17 June 1998 Simon Fraser University Harbour Center, Canada 1998 ppsn92:oReilly inproceedings Genetic Programming (GP) has recently been introduced by John R. Koza as a method for genetically breeding populations of computer programs to solve problems. We believe GP to constitute a significant extension of the Genetic Algorithm (GA) research paradigm primarily because it generalizes the genetic search techniques: instead of looking for a solution to a specific instance of a problem, GP attempts to evolve a program capable of computing the solutions for any instance of the problem. We have implemented a genetic programming environment, GP*, that is capable of duplicating Koza`s experiments. In this paper we describe a specific GP experiment on the evolution of programs to sort vectors, and discuss the issues that must be addressed in any application of GP: the design of fitness functions and test suites, and the selection of program terminals and functions. Our observations point to several previously unnoticed shortcomings of the GP approach. We hypothesize that these shortcomings are due to the fact that GP only uses a hierarchical representation but does not construct its solutions in an explicitly hierarchical manner. genetic algorithms genetic programming 331--340 Parallel Problem Solving from Nature 2 http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ppsn92.ps.gz Elsevier Science 10 pages An Experimental Perspective on Genetic Programming R Manner and B Manderick Brussels, Belgium September 28 - 30 1992 Critical of Koza's GP (nb non-ADF) {"}We conclude that GP in its current form is heirarchical only with respect to its representation and not with resepect to its process of constructing solutions. This limits the ability of GP to evolve complex programs from simple, general functions, and makes the algorithm stongly dependant on initial human design decisions.{"} Proposes SPECIALISE and DECOMPOSE operators, like encapsulate and expand, but applied infrequently and depending upon how the GP is going. SPECIALISE would look for common code in better programs and convert them to functions which cannot be disrupted by crossover. However: ``Regarding the specialize and decompose operators, we abandoned them after very preliminary work''. References Ken De Jong ICGA-87 1992-09 oreilly:1998:fssaGP inproceedings We define fitness structure in genetic programming to be the mapping between the subprograms of a program and their respective fitness values. This paper shows how various fitness structures of a problem with independent subsolutions relate to the acquisition of subsolutions. The rate of subsolution acquisition is found to be directly correlated with fitness structure whether that structure is uniform, linear or exponential. An understanding of fitness structure provides partial insight into the complicated relationship between fitness function and the outcome of genetic programming's search. genetic algorithms genetic programming 269--277 Genetic Programming 1998: Proceedings of the Third Annual Conference San Francisco, CA, USA 9 pages Morgan Kaufmann http://www.ai.mit.edu/people/unamay/papers/timing-final.ps How Fitness Structure Affects Subsolution Acquisition in Genetic Programming John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo University of Wisconsin, Madison, Wisconsin, USA 1-55860-548-7 22-25 July 1998 GP-98 1998 stephenson03 inproceedings Genetic programming (GP) has a natural niche in the optimization of small but high payoff software heuristics. We use GP to optimize the priority functions associated with two well known compiler heuristics: predicated hyperblock formation, and register allocation. Our system achieves impressive speedups over a standard baseline for both problems. For hyperblock selection, application-specific heuristics obtain an average speedup of 23percent (up to 73percent) for the applications in our suite. By evolving the compiler's heuristic over several benchmarks, the best general-purpose heuristic our system found improves the predication algorithm by an average of 25percent on our training set, and 9percent on a completely unrelated test set. We also improve a well-studied register allocation heuristic. On average, our system obtains a 6percent speedup when it specializes the register allocation algorithm for individual applications. The general-purpose heuristic for register allocation achieves a 3percent improvement. LNCS genetic algorithms genetic programming SBSE 238--253 EvoNet Genetic Programming, Proceedings of EuroGP'2003 Berlin http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=238 Springer-Verlag Genetic Programming Applied to Compiler Heuristic Optimization Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa Essex 3-540-00971-X 2610 14-16 April 2003 EuroGP'2003 held in conjunction with EvoWorkshops 2003 2003 o2007integrating article Genetic Programming and Evolvable Machines Integrating generative growth and evolutionary computation for form exploration 163--186 8 2007 2 Springer 2007 o1998preliminary article Artificial life six A preliminary investigation of evolution as a form design strategy 443 6 1998 The MIT Press 1998 o1996investigating inproceedings MIT Press Investigating the generality of automatically defined functions 351--356 1996 Proceedings of the first annual conference on genetic programming 1996 hemberg:2001:adtsg2 inproceedings Conor Ryan {GENR8} - {A} Design Tool for Surface Generation genetic algorithms genetic programming San Francisco, California, USA 413--416 7 July 2001 Graduate Student Workshop GECCO-2001WKS Part of heckendorn:2001:GECCOWKS, see \cite{hemberg:2001:adtsg}, GENR8 2001 hemberg:2002:gecco:workshop inproceedings genetic algorithms genetic programming grammatical evolution 120--123 {GECCO 2002}: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference 445 Burgess Drive, Menlo Park, CA 94025 http://www.ai.mit.edu/projects/emergentDesign/genr8/gecco2002.pdf AAAI 4 pages {GENR8} - Using Grammatical Evolution In {A} Surface Design Tool Alwyn M. Barry New York 8 July 2002 Bird-of-a-feather Workshops, GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) part of barry:2002:GECCO:workshop 2002 hemberg:2001:adtsg inproceedings Erik D. Goodman {GENR8} - {A} Design Tool for Surface Generation genetic algorithms genetic programming architecture Lindenmayer systems BNF grammar HEMLS grammatical evolution Alias|Wavefront Maya San Francisco, California, USA 160--167 9-11 July 2001 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers GECCO-2001LB http://www.ai.mit.edu/projects/emergentDesign/genr8/lateGecco.pdf 2001 spector1999advances article Mit Press Complex Adaptive Systems Series Advances in genetic programming: volume 3 476 1999 1999 semet2004interactive inproceedings Springer An interactive artificial ant approach to non-photorealistic rendering 188--200 2004 Genetic and Evolutionary Computation--GECCO 2004 2004 kim2007genetic article Applications of Evolutionary Computing Genetic representations for evolutionary of network coding resources 21--31 2007 Springer 2007 o1998impact article Advances in Artificial Intelligence The impact of external dependency in genetic programming primitives 154--168 1998 Springer 1998 o1998impactIEEE inproceedings IEEE The Impact of External Dependency Dependency in Genetic Programming Primitives 306 1 1998 1998 IEEE International Conference on Evolutionary Computation proceedings: IEEE World Congress on Computational Intelligence, May 4-May 9, 1998, Anchorage, Alaska, USA. 1998 Bentley:2001:geccowks inproceedings A perfect creative evolutionary design system is impossible to achieve, but in this position paper we discuss 10 steps that might bring us a little closer to this dream. These important problems and requirements have been identified as a result of both authors{\^a} experiences on a number of projects in this area. While our solutions may not solve all of the problems, they illustrate what we regard as the current state of the art in creative evolutionary design. Peter Bentley and Mary Lou Maher and Josiah Poon Ten steps to make a perfect creative evolutionary design system genetic algorithms genetic programming Agency GP design evolutionary SYSTEM SYSTEMS WORKSHOP 7 July 2001 Non-Routine Design with Evolutionary Systems, GECCO-2001 Workshop http://sydney.edu.au/engineering/it/~josiah/gecco2001_workshop_schedule.html http://sydney.edu.au/engineering/it/~josiah/gecco_workshop_bentley.pdf 7 pages 2001 kim2007doubly inproceedings ACM A doubly distributed genetic algorithm for network coding 1272--1279 2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation 2007 o2005genetic book Genetic programming theory and practice II 2 2005 Springer-Verlag New York Inc 2005 hemberg2004extending article Genetic Programming Extending grammatical evolution to evolve digital surfaces with genr8 299--308 2004 Springer 2004 o2000emergent article Artificial life seven Emergent design: Artificial life for architecture design 454 2000 The MIT Press 2000 sastry2005population article Genetic programming theory and practice II Population Sizing for Genetic Programming Based on Decision-Making 49--65 2005 Springer 2005 testa2001emergent article Environment and Planning B Emergent Design: a crosscutting research program and design curriculum integrating architecture and artificial intelligence 481--498 28 2001 4 2001 aggarwal2007simulation inproceedings EDA Consortium Simulation-based reusable posynomial models for MOS transistor parameters 69--74 2007 Proceedings of the conference on Design, automation and test in Europe 2007 kim2007coding inproceedings IEEE On the coding-link cost tradeoff in multicast network coding 1--7 2007 Military Communications Conference, 2007. MILCOM 2007. IEEE 2007 terry2006grace article Evohot: Applications of Evolutionary Computing Grace: Generative robust analog circuit exploration 332--343 2006 Springer 2006 o1993expressiveness inproceedings Springer The expressiveness of silence: Tight bounds for synchronous communication of information using bits and silence 321--332 1993 Graph-Theoretic Concepts in Computer Science 1993 aggarwal2007design article Genetic Programming Theory and Practice IV Design of posynomial models for MOSFETs: symbolic regression using genetic algorithms 219--236 2007 Springer 2007 brooks1999technologies inproceedings Technologies for human/humanoid natural interaction 1999 The 2nd International Symposium in HUmanoid RObots (HURO'99), Tokyo, Japan 1999 o2000representation article Proceedings of Adaptive Computing in Design and Manufacture (ACDM 2000) Representation in architectural design tools 2000 2000 aggarwal2006filter inproceedings ACM Filter approximation using explicit time and frequency domain specifications 753--760 2006 Proceedings of the 8th annual conference on Genetic and evolutionary computation 2006 puppin2004adapting article Languages and Compilers for Parallel Computing Adapting convergent scheduling using machine-learning 17--31 2004 Springer 2004 o2004genetic article Genetic Programming Theory And Practice Ii (Genetic Programming) 2004 Springer-Verlag Telos 2004 kim2009network inproceedings Institute of Electrical and Electronics Engineers Network coding and its implications on optical networking 2009 Optical Fiber Communication Conference http://hdl.handle.net/1721.1/59470 2009 durrett2011computational inproceedings ACM Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics 69--80 2011 Proceedings of the 11th workshop proceedings on Foundations of Genetic Algorithms (FOGA XI) 2011 kim2009constrainedgenetic article Genetic Programming Theory and Practice VI Constrained Genetic Programming to Minimize Overfitting in Stock Selection 1--16 2009 Springer 2009 aggarwal2006self article A self-tuning analog proportional-integral-derivative (pid) controller 2006 IEEE Computer Society 2006 thompson2006investigation inproceedings An Investigation into the Perception of Colors under Dynamic Modulation of Color Rendering in Real Life Settings 2006 Proceeding of the CIE Expert Symposium in Visual Appearance, Paris 2006 becker2009genetic inproceedings ACM Genetic programming for quantitative stock selection 9--16 2009 Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation 2009 o2005genetic article Genetic programming theory and practice II Genetic Programming: Theory And Practice 1--10 2005 Springer 2005 springerlink:10.1007/978-3-540-72877-1_8 incollection We present the computational design tool Genr8 and six different architectural projects making extensive use of Genr8. Genr8 is based on ideas from Evolutionary Computation (EC) and Artificial Life and it produces surfaces using an organic growth algorithm inspired by how plants grow. These algorithms have been implemented as an architect?s design tool and the chapter provides an illustration of the possibilities that the tool provides. Natural Computing Series Computer Science 167-188 The Art of Artificial Evolution http://dx.doi.org/10.1007/978-3-540-72877-1_8 Springer Berlin Heidelberg Genr8: Architects? Experience with an Emergent Design Tool Romero, Juan and Machado, Penousal 978-3-540-72877-1 2008 Imperial College Department of Bioengineering Exhibition Road London SW7 2AZ UK 10.1007/978-3-540-72877-1_8 2008 beyergecco misc GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, Washington DC, USA, 25-29 June 2005 2005 ACM Press. ISBN 2005 thompson2006investigation inproceedings An Investigation into the Perception of Color under LED White Composite Spectra with Modulated Color Rendering 2006 Proceedings for the 6th Light Research Office Symposium in Light and Color, Lake Buena Vista, FL 2006 o'reilly:2001:aagpd inproceedings Erik D. Goodman {Agency-GP:} Agent-Based Genetic Programming for Design genetic algorithms genetic programming San Francisco, California, USA 303--309 9-11 July 2001 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers GECCO-2001LB 2001 testa2001agency inproceedings AGENCY GP: Agent-Based Genetic Programming for Spatial Exploration 2001 Generative Evolutionary Computer Conference 2001 aggarwal2007cosmo inproceedings ACM COSMO: a correlation sensitive mutation operator for multi-objective optimization 741--748 2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation 2007 aggarwal2007monotonicity article In submission to ICCAD Monotonicity information in Simulation-based approaches for efficient circuit sizing and hierarchical synthesis 2007 2007 o1997trends article Evolutionary Computation Trends in evolutionary methods for program induction 5 1997 2 1997 vladislavleva2010knowledge inproceedings ACM Knowledge mining with genetic programming methods for variable selection in flavor design 941--948 2010 Proceedings of the 12th annual conference on Genetic and Evolutionary Computation, GECCO'10 2010 o2010gptp article Genetic Programming Theory and Practice VII GPTP 2009: An example of evolvability 1--18 2010 Springer 2010 cantugenetic inproceedings Genetic and Evolutionary Computation-GECCO 2003, Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16, 2003 2003 Proceedings 2003 o2000asynchronous inproceedings Asynchronous to synchronous transformations 265--281 2000 Proceedings of the Fourth International Conference on Principles of Distributed Systems, Paris 2000 edsinger2002face misc A Face for a Humanoid Robot 2002 MIT Memo 2002 bliss2007analysis inproceedings Analysis and Mapping of Sparse Matrix Computations 2007 HPEC 2007 Workshop, Lexington, MA 2007 riolo2009genetic article Ann Arbor, Springer Genetic Programming Theory and Practice VII, Genetic and Evolutionary Computation 14--16 2009 2009 o2003tight article Discrete applied mathematics Tight bounds for synchronous communication of information using bits and silence 195--209 129 2003 1 Elsevier 2003 springerlink:10.1007/978-3-642-12148-7_21 incollection We describe a data mining framework that derives panelist information from sparse flavour survey data. One component of the framework executes genetic programming ensemble based symbolic regression. Its evolved models for each panelist provide a second component with all plausible and uncorrelated explanations of how a panelist rates flavours. The second component bootstraps the data using an ensemble selected from the evolved models, forms a probability density function for each panelist and clusters the panelists into segments that are easy to please, neutral, and hard to please. Lecture Notes in Computer Science 244-255 European Conference on Genetic Programming, EuroGP 2010 http://dx.doi.org/10.1007/978-3-642-12148-7_21 Springer Learning a Lot from Only a Little: Genetic Programming for Panel Segmentation on Sparse Sensory Evaluation Data Esparcia-Alc?zar, Anna and Ek?rt, Anik? and Silva, Sara and Dignum, Stephen and Uyar, A. 6021 2010 University of Antwerp Belgium 10.1007/978-3-642-12148-7_21 2010 veeramachaneni2010evolutionary inproceedings ACM Evolutionary optimization of flavors 1291--1298 2010 Proceedings of the 12th annual conference on Genetic and Evolutionary Computation, GECCO'10 2010 oppacher1995hybridized techreport Hybridized Crossover-Based Search Techniques for Program Discovery 95-02-007 1995 Working Papers http://www.santafe.edu/media/workingpapers/95-02-007.pdf Santa Fe Institute 1995 budynek2008methods misc Methods and Apparatus for Interactive Name Searching Techniques May~12 2008 US Patent App. 20,090/070,320 2008-05 o2010hogs article Parallel Computing Hogs and slackers: Using operations balance in a genetic algorithm to optimize sparse algebra computation on distributed architectures 635--644 36 2010 10-11 Elsevier Science Publishers BV 2010 durrett2010genetic article Evolutionary Computation in Combinatorial Optimization A Genetic Algorithm to Minimize Chromatic Entropy 59--70 2010 Springer 2010 wagneroptimizing article Scientific Proceedings of European Wind Energy Association Conference (EWEA 2011) Optimizing the Layout of 1000 Wind Turbines 2011 2011 chenSVMMengThesis phdthesis Offline and online SVM performance analysis M.Eng Thesis, Massachusetts Institute of Technology 2007 http://hdl.handle.net/1721.1/41259 2007 Aggarwal2007Thesis phdthesis Analog circuit optimization using evolutionary algorithms and convex optimization Masters Thesis, Massachusetts Institute of Technology 2007 http://hdl.handle.net/1721.1/40525 2007 soule2009genetic article Genetic Programming Theory and Practice VI Genetic programming: Theory and practice 1--18 2009 Springer 2009 breazeal1999natural misc Natural Tasking of Robots Based on Human Interaction Cues 1999 http://www.cs.yale.edu/homes/Scassellati/abstracts/1999/scaz4.pdf 1999 Roy07CDIO inproceedings The Experience of Teaching Software Development in a Robotics Project Course Cambridge, MA June 2007 Proceedings of the Third International CDIO Conference and Collaborators' Meeting 2007-06 McDermott, James McDermott McDermott, James Arnold, D.V. Arnold Arnold, D.V. Spector, L. Spector Spector, L. Buchsbaum, D. Buchsbaum Buchsbaum, D. {O'Reilly}, {Una-May} {O'Reilly} {Una-May} {O'Reilly} Sastry, K. Sastry Sastry, K. Rus, Daniela Rus Daniela Rus Bonabeau, E.W. Bonabeau Bonabeau, E.W. Beyer, H.G. Beyer Beyer, H.G. Vladislavleva, Ekaterina Vladislavleva Vladislavleva, Ekaterina Brooks, R.A. Brooks Brooks, R.A. Breazeal, C. Breazeal Breazeal, C. Cantu-Paz, E. Cantu-Paz Cantu-Paz, E. Soule, T. Soule Soule, T. Angeline, P.J. Angeline Angeline, P.J. Bentley, Peter J. Bentley Peter J. Bentley Kim, Minkyu Kim Minkyu Kim others others others Oppacher, F. Oppacher Oppacher, F. M{\'e}dard, M. M{\'e}dard M{\'e}dard, M. Foster, J.A. Foster Foster, J.A. Goldberg, David E. Goldberg David E. Goldberg Scassellati, B. Scassellati Scassellati, B. Testa, P. Testa Testa, P. Neumann, F. Neumann Neumann, F. Amarasinghe, S. Amarasinghe Amarasinghe, S. Martin, M. Martin Martin, M. Medard, M. Medard Medard, M. Parcon, J. Parcon Parcon, J. Blum, C. Blum Blum, C. Kendall, G. Kendall Kendall, G. Veeramachaneni, K. Veeramachaneni Veeramachaneni, K. McConaghy, T. McConaghy McConaghy, T. Leonard, John Leonard John Leonard Pacula, Maciej Pacula Maciej Pacula Fei, P. Fei Fei, P. Greenwold, S. Greenwold Greenwold, S. Becker, Y.L. Becker Becker, Y.L. Weber, J. Weber Weber, J. Menges, Achim Menges Menges, Achim Langdon, William B. Langdon William B. Langdon Thompson, M. Thompson Thompson, M. Mullen, Julie Mullen Julie Mullen Greenwold, Simon Greenwold Simon Greenwold Varshavskaya, P. Varshavskaya Varshavskaya, P. Thinniyam, R.S. Thinniyam Thinniyam, R.S. Testa, Peter Testa Peter Testa Chen, K.F. Chen Chen, K.F. Mohindra, S. Mohindra Mohindra, S. Brooks, R. Brooks Brooks, R. Jonas, Katrin Jonas Jonas, Katrin Mohindra, Sanjeev Mohindra Sanjeev Mohindra Jin, W.O. Jin Jin, W.O. Riolo, R.L. Riolo Riolo, R.L. Burland, Matt Burland Burland, Matt Worzel, B. Worzel Worzel, B. Bonabeau, E. Bonabeau Bonabeau, E. Wagner, Markus Wagner Markus Wagner Oppacher, Franz Oppacher Franz Oppacher Amarasinghe, Saman Amarasinghe Saman Amarasinghe Bullock, Noah Bullock Noah Bullock Riolo, R. Riolo Riolo, R. O'Reilly, U.M. O'Reilly O'Reilly, U.M. O'Reilly, Una-May O'Reilly Una-May O'Reilly Stephenson, M. Stephenson Stephenson, M. Semet, Y. Semet Semet, Y. Kemp, C. Kemp Kemp, C. Stephenson, Mark Stephenson Mark Stephenson Goldberg, D. Goldberg Goldberg, D. Farrell, M. Farrell Farrell, M. Kim, M. Kim Kim, M. Edsinger, A. Edsinger Edsinger, A. Bliss, Nadya Bliss Nadya Bliss Yu, T. Yu Yu, T. Ansel, Jason Ansel Jason Ansel Ahn, Chang Wook Ahn Chang Wook Ahn Durand, F. Durand Durand, F. Dasgupta, D. Dasgupta Dasgupta, D. Vladislavleva, K. Vladislavleva Vladislavleva, K. Vanneschi, Leonardo Vanneschi Vanneschi, Leonardo Funes, P. Funes Funes, P. Parcon, Jason Parcon Parcon, Jason Poli, Riccardo Poli Riccardo Poli Fitzpatrick, P. Fitzpatrick Fitzpatrick, P. Budynek, J. Budynek Budynek, J. Effros, M. Effros Effros, M. Bliss, N. Bliss Bliss, N. Mullen, J. Mullen Mullen, J. Aryananda, L. Aryananda Aryananda, L. Torres-Jara, E. Torres-Jara Torres-Jara, E. Roy, Nicholas Roy Nicholas Roy Durrett, G. Durrett Durrett, G. Neumann, Frank Neumann Frank Neumann Santoro, N. Santoro Santoro, N. Veeramachaneni, Kalyan Veeramachaneni Veeramachaneni, Kalyan Ross, I. Ross Ross, I. Noel, Pierre-Luc Noel Pierre-Luc Noel Robinson, Eric Robinson Eric Robinson Hemberg, Martin Hemberg Martin Hemberg Bliss, N.T. Bliss Bliss, N.T. Burland, M. Burland Burland, M. Robinson, E. Robinson Robinson, E. Aggarwal, V. Aggarwal Aggarwal, V. Terry, M. Terry Terry, M. O'Reilly, U.M. (advisor) O'Reilly O'Reilly, U.M. (advisor) Fuchs, Steven R. Fuchs Fuchs, Steven R. Wilson, S.W. Wilson Wilson, S.W. O'Reilly, U.-M. O'Reilly O'Reilly, U.-M. Puppin, D. Puppin Puppin, D. Wagner, M. Wagner Wagner, M. Hemberg, M. Hemberg Hemberg, M. Weiser, D. Weiser Weiser, D. Vladislavleva, Katya Vladislavleva Vladislavleva, Katya Mao, M. Mao Mao, M. Teller, Seth Teller Seth Teller Langdon, W.B. Langdon Langdon, W.B. Gon?alves, Michel da Costa Gon?alves Gon?alves, Michel da Costa Sherry, Dylan Sherry Dylan Sherry Kim, W. Kim Kim, W. Martens, S. Martens Martens, S. Kalyan Veeramachaneni, Kalyan Veeramachaneni Kalyan Veeramachaneni, Standish, R.K. Standish Standish, R.K. Davis, L. Davis Davis, L. Ramachandran, G. Ramachandran Ramachandran, G. Roy, R. Roy Roy, R. Martin, Martin C. Martin Martin C. Martin Deb, K. Deb Deb, K. Kim, Wonsik Kim Wonsik Kim Marcus, J. Marcus Marcus, J. Banzhaf, W. Banzhaf Banzhaf, W. Nordin, Peter Nordin Peter Nordin
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Publications of Una-May O'Reilly
Evolutionary Design and Optimization Group, CSAIL, MIT