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%volume = {5914},
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%  pages={2528--2536},
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% pages = {565--574},

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%  Pentland, Alex Sandy and Adamic, Lada and Aral, Sinan and Barabasi, Albert Laszlo and Brewer, Devon and Christakis, Nicholas and Contractor, Noshir and Fowler, James and Gutmann, Myron and others},

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%  Rast, Jonathan P and Oliveri, Paola and Ransick, Andrew and Calestani, Cristina and Yuh, Chiou-Hwa and Minokawa, Takuya and Amore, Gabriele and Hinman, Veronica and Arenas-Mena, Cesar and others},

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%  author={LeCun, Yann and Boser, Bernhard and Denker, John S and Henderson, Donnie and Howard, Richard E and Hubbard, Wayne and Jackel, Lawrence D},
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%  pages={269--276},

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@article{bronstein2006generalized,
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  author={Bronstein, A. M. and Bronstein, M. M. and Kimmel, R.},
  journal={PNAS},
  volume={103},
  number={5},
  pages={1168--1172},
  year={2006},
}

@article{kim2011blended,
  title={Blended intrinsic maps},
  author={Kim, V. and Lipman, Y. and Funkhouser, T.},
  journal={ACM Trans. Graphics},
  volume={30},
  number={4},
  pages={79},
  year={2011},
}

@article{bronstein2011shape,
  title={{ShapeGoogle}: Geometric words and expressions for invariant shape retrieval},
  author={Bronstein, A. M. and Bronstein, M. M. and Guibas, L. J. and Ovsjanikov, M.},
  journal={ACM Trans. Graphics},
  volume={30},
  number={1},
  pages={1},
  year={2011},
}

@article{maaten2008visualizing,
  title={Visualizing data using t-{SNE}},
  author={Maaten, L. and Hinton, G.},
  journal={JMLR},
  volume={9},
  pages={2579--2605},
  year={2008}
}

@article{belkin2003laplacian,
  title={Laplacian eigenmaps for dimensionality reduction and data representation},
  author={Belkin, M. and Niyogi, P.},
  journal={Neural Computation},
  volume={15},
  number={6},
  pages={1373--1396},
  year={2003},
}

@article{coifman2006diffusion,
  title={Diffusion maps},
  author={Coifman, R. R. and Lafon, S.},
  journal={App. and Comp. Harmonic Analysis},
  volume={21},
  number={1},
  pages={5--30},
  year={2006},
}

@article{nash1956imbedding,
  title={The imbedding problem for {R}iemannian manifolds},
  author={Nash, J.},
  journal={Annals of Mathematics},
  volume={63},
  number={1},
  pages={20--63},
  year={1956},
}

@article{tenenbaum2000global,
  title={A global geometric framework for nonlinear dimensionality reduction},
  author={Tenenbaum, J. B. and De Silva, V. and Langford, J. C.},
  journal={Science},
  volume={290},
  number={5500},
  pages={2319--2323},
  year={2000},
}

@article{roweis2000nonlinear,
  title={Nonlinear dimensionality reduction by locally linear embedding},
  author={Roweis, S. T. and Saul, L. K.},
  journal={Science},
  volume={290},
  number={5500},
  pages={2323--2326},
  year={2000},
}

@inproceedings{hadsell2006dimensionality,
  title={Dimensionality reduction by learning an invariant mapping},
  author={Hadsell, R. and Chopra, S. and LeCun, Y.},
  booktitle={Proc. CVPR},
  year={2006},
}

@inproceedings{cho2011friendship,
  title={Friendship and mobility: user movement in location-based social networks},
  author={Cho, E. and Myers, S. A. and Leskovec, J.},
  booktitle={Proc. KDD},
  year={2011},
}

@article{mikolov2013distributed,
  title={Distributed representations of words and phrases and their compositionality},
  author={Mikolov, T. and Dean, J.},
  journal={Proc. NIPS},
  year={2013}
}

@inproceedings{perozzi2014deepwalk,
  title={{DeepWalk}: Online learning of social representations},
  author={Perozzi, B. and Al-Rfou, R. and Skiena, S.},
  booktitle={Proc. KDD},
  year={2014},
}

@inproceedings{tang2015line,
  title={{LINE}: Large-scale information network embedding},
  author={Tang, J. and Qu, M. and Wang, M. and Zhang, M. and Yan, J. and Mei, Q.},
  booktitle={Proc. WWW},
  year={2015},
}

@inproceedings{cao2015grarep,
  title={{GraRep}: Learning graph representations with global structural information},
  author={Cao, S. and Lu, W. and Xu, Q.},
  booktitle={Proc. IKM},
  year={2015},
}

@inproceedings{tombari2010unique,
  title={Unique signatures of histograms for local surface description},
  author={Tombari, F. and Salti, S. and Di Stefano, L.},
  booktitle={Proc. ECCV},
  year={2010},
}

@article{shuman2013emerging,
  title={The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains},
  author={Shuman, D. I. and Narang, S. K. and Frossard, P. and Ortega, A. and Vandergheynst, P.},
  journal={{IEEE} Sig. Proc. Magazine},
  volume={30},
  number={3},
  pages={83--98},
  year={2013},
}

@article{lim2015hodge,
  title={Hodge {L}aplacians on graphs},
  author={Lim, L.-H.},
  journal={arXiv:1507.05379},
  year={2015}
}

@incollection{wardetzky2008convergence,
  title={Convergence of the cotangent formula: An overview},
  author={Wardetzky, M.},
  booktitle={Discrete Differential Geometry},
  pages={275--286},
  year={2008},
}

@inproceedings{kovnatsky2013coupled,
  title={Coupled quasi-harmonic bases},
  author={Kovnatsky, A. and Bronstein, M. M. and Bronstein, A. M. and Glashoff, K. and Kimmel, R.},
  booktitle={Computer Graphics Forum},
  volume={32},
  number={2},
  pages={439--448},
  year={2013},
}

@article{eynard2015multimodal,
  title={Multimodal manifold analysis by simultaneous diagonalization of {L}aplacians},
  author={Eynard, D. and Kovnatsky, A. and Bronstein, M. M. and Glashoff, K. and Bronstein, A. M.},
  journal={Trans. PAMI},
  volume={37},
  number={12},
  pages={2505--2517},
  year={2015},
}

@inproceedings{boscaini2015shape,
  title={Shape-from-Operator: Recovering Shapes from Intrinsic Operators},
  author={Boscaini, D. and Eynard, D. and Kourounis, D. and Bronstein, M. M.},
  booktitle={Computer Graphics Forum},
  volume={34},
  number={2},
  pages={265--274},
  year={2015},
}

@article{shuman2016vertex,
  title={Vertex-frequency analysis on graphs},
  author={Shuman, D. I. and Ricaud, B. and Vandergheynst, P.},
  journal={App. and Comp. Harmonic Analysis},
  volume={40},
  number={2},
  pages={260--291},
  year={2016},
}

@inproceedings{boscaini2015learning,
  title={Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks},
  author={Boscaini, D. and Masci, J. and Melzi, S. and Bronstein, M. M. and Castellani, U. and Vandergheynst, P.},
  booktitle={Computer Graphics Forum},
  volume={34},
  number={5},
  pages={13--23},
  year={2015},
}

@article{welling2016,
  title={Semi-Supervised Classification with Graph Convolutional Networks},
  author={T. N. Kipf and M. Welling},
  journal={arXiv:1609.02907},
  year={2016}
}

@inproceedings{gori2005new,
  title={A new model for learning in graph domains},
  author={Gori, M. and Monfardini, G. and Scarselli, F.},
  booktitle={Proc. IJCNN},
  year={2005},
}

@article{sen2008collective,
  title={Collective classification in network data},
  author={Sen, P. and Namata, G. and Bilgic, M. and Getoor, L. and Galligher, B. and Eliassi-Rad, T.},
  journal={AI Magazine},
  volume={29},
  number={3},
  pages={93},
  year={2008}
}

@inproceedings{biasotti2015recent,
  title={Recent trends, applications, and perspectives in {3D} shape similarity assessment},
  author={Biasotti, S. and Cerri, A. and Bronstein, A. M. and Bronstein, M. M.},
  booktitle={Computer Graphics Forum},
  year={2015},
}

@inproceedings{rodola2014dense,
  title={Dense non-rigid shape correspondence using random forests},
  author={Rodol{\`a}, Emanuele and Rota Bulo, Samuel and Windheuser, Thomas and Vestner, Matthias and Cremers, Daniel},
  booktitle={Proc. CVPR},
  year={2014}
}

@inproceedings{duvenaud2015convolutional,
  title={Convolutional networks on graphs for learning molecular fingerprints},
  author={Duvenaud, David K and Maclaurin, Dougal and Iparraguirre, Jorge and Bombarell, Rafael and Hirzel, Timothy and Aspuru-Guzik, Al{\'a}n and Adams, Ryan P},
  booktitle={Proc. NIPS},
  year={2015}
}

@article{litman2014learning,
  title={Learning spectral descriptors for deformable shape correspondence},
  author={Litman, Roee and Bronstein, Alexander M},
  journal={Trans. PAMI},
  volume={36},
  number={1},
  pages={171--180},
  year={2014},
}

@inproceedings{dosovitskiy2015flownet,
  title={Flownet: Learning optical flow with convolutional networks},
  author={Dosovitskiy, Alexey and Fischery, Philipp and Ilg, Eddy and Hazirbas, Caner and Golkov, Vladimir and van der Smagt, Patrick and Cremers, Daniel and Brox, Thomas and others},
  booktitle={Proc. ICCV},
  year={2015},
}

@inproceedings{wardetzky2007discrete,
  title={Discrete Laplace operators: no free lunch},
  author={Wardetzky, Max and Mathur, Saurabh and K{\"a}lberer, Felix and Grinspun, Eitan},
  booktitle={Proc. SGP},
  year={2007}
}

@article{von2007tutorial,
  title={A tutorial on spectral clustering},
  author={Von Luxburg, Ulrike},
  journal={Statistics and Computing},
  volume={17},
  number={4},
  pages={395--416},
  year={2007},
}

@article{dieleman2016wavenet,
  title={WaveNet: A Generative Model for Raw Audio},
  author={Dieleman, Sander and Zen, Heiga and Simonyan, Karen and Vinyals, Oriol and Graves, Alex and Kalchbrenner, Nal and Senior, Andrew and Kavukcuoglu, Koray},
  journal={arXiv:1609.03499},
  year={2016}
}

@inproceedings{dosovitskiy2016learning,
  title={Learning to Generate Chairs, Tables and Cars with Convolutional Networks},
  author={Dosovitskiy, Alexey and Springenberg, Jost and Tatarchenko, Maxim and Brox, Thomas},
  booktitle={Proc. CVPR},
  year={2015},
}


text:
LICENSE = "GPL"
MAINTAINER = "Patrick Steiner <patrick.steiner@a1.net>"

DEPENDS = "gtk+"

inherit gnome pkgconfig

do_stage() {
autotools_stage_all
}

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bb


text:
SECTION = "base"
PR = "r0"
include fakeroot_${PV}.bb
inherit native

SRC_URI += "file://fix-prefix.patch;patch=1"
S = ${WORKDIR}/fakeroot-${PV}

EXTRA_OECONF = "--program-prefix="

do_stage_append () {
    oe_libinstall -so libfakeroot ${STAGING_LIBDIR}/libfakeroot/
}

repository: BackupTheBerlios/openslug-svn

repo_name: BackupTheBerlios/openslug-svn


text:
package goexercises

// OddOccurrences - https://codility.com/programmers/lessons/2-arrays/odd_occurrences_in_array/start/
// Find value that occurs in odd number of elements.
func OddOccurrences(arr []int) int {
	m := map[int]int{}
	for _, v := range arr {
		m[v]++
	}
	for k, v := range m {
		if v%2 != 0 {
			return k
		}
	}
	return 0
}

repository: awantik/goexercises

repo_name: awantik/goexercises

extension: go


text:
SUMMARY = "cve-check-tool"
DESCRIPTION = "cve-check-tool is a tool for checking known (public) CVEs.\
The tool will identify potentially vunlnerable software packages within Linux distributions through version matching."
HOMEPAGE = "https://github.com/ikeydoherty/cve-check-tool"
SECTION = "Development/Tools"
LICENSE = "GPL-2.0"
LIC_FILES_CHKSUM = "file://LICENSE;md5=e8c1458438ead3c34974bc0be3a03ed6"

SRCREV = "cbc2d0e4fe56c5c82100c249badd8620fe2b22a2"
S = "${WORKDIR}/git"

BRANCH = "master"
SRC_URI = "git://github.com/ikeydoherty/cve-check-tool.git;protocol=git;branch=${BRANCH}"

DEPENDS = "libcheck glib-2.0 json-glib curl libxml2 sqlite3 openssl ca-certificates"

inherit pkgconfig autotools

EXTRA_OECONF = "--disable-static --enable-relative-plugins"
CFLAGS += "-Wno-error=pedantic"

BBCLASSEXTEND = "native nativesdk"

repository: MentorEmbedded/meta-security-isafw

repo_name: MentorEmbedded/meta-security-isafw

gha_license_id: MIT

gha_language: Python


text:
@Article{Helmuth:2015:ieeeTEC,
	author =	"Thomas Helmuth and Lee Spector and James Matheson",
	title =	"Solving Uncompromising Problems with Lexicase
	Selection",
	journal =	"IEEE Transactions on Evolutionary Computation",
	year = 	"2015",
	volume =	"19",
	number =	"5",
	pages =	"630--643",
	month =	oct,
	keywords =	"genetic algorithms, genetic programming, parent
	selection, lexicase selection, tournament selection,
	PushGP",
	ISSN = 	"1089-778X",
	URL =  	"http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6920034",
	doi =  	"doi:10.1109/TEVC.2014.2362729",
	size = 	"14 pages",
	abstract =	"We describe a broad class of problems, called
	uncompromising problems, characterised by the
	requirement that solutions must perform optimally on
	each of many test cases. Many of the problems that have
	long motivated genetic programming research, including
	the automation of many traditional programming tasks,
	are uncompromising. We describe and analyse the
	recently proposed lexicase parent selection algorition
	and show that it can facilitate the solution of
	uncompromising problems by genetic programming. Unlike
	most traditional parent selection techniques, lexicase
	selection does not base selection on a fitness value
	that is aggregated over all test cases; rather, it
	considers test cases one at a time in random order. We
	present results comparing lexicase selection to more
	traditional parent selection methods, including
	standard tournament selection and implicit fitness
	sharing, on four uncompromising problems: finding terms
	in finite algebras, designing digital multipliers,
	counting words in files, and performing symbolic
	regression of the factorial function. We provide
	evidence that lexicase selection maintains higher
	levels of population diversity than other selection
	methods, which may partially explain its utility as a
	parent selection algorithm in the context of
	uncompromising problems.",
	notes =	"also known as \cite{6920034}",
	doi-url =	"http://dx.doi.org/10.1109/TEVC.2014.2362729",
}

@incollection{Helmuth:2015:GPTP,
	author = {Thomas Helmuth and Nicholas Freitag McPhee and Lee Spector},
	title = {Lexicase selection for program synthesis: a diversity analysis},
	booktitle = {Genetic Programming Theory and Practice XIII},
	date = {2015},
	NOeditor = {?},
	series = {Genetic and Evolutionary Computation},
	publisher = {Springer},
	pubstate = {In press}
}

@phdthesis{Helmuth:2015:dissertation,
	author = {Thomas Helmuth},
	title = {General Program Synthesis from Examples Using Genetic Programming with Parent Selection Based on Random Lexicographic Orderings of Test Cases},
	type = {{Ph.D.} dissertation},
	institution = {University of Massachusetts, Amherst},
	date = {September, 2015},
	year = {2015},
	url = {http://scholarworks.umass.edu/dissertations_2/465/},
}

@inproceedings{Helmuth:2015:GECCO,
	author = {Thomas Helmuth and Lee Spector},
	NOeditor = {Many People},
	title = {General program synthesis benchmark suite},
	booktitle = {GECCO '15: Proceedings of the 2015 Conference on
	Genetic and Evolutionary Computation},
	date = {July, 2015},
	year = {2015},
	eventtitle = {Conference on Genetic and Evolutionary Computation},
	eventdate = {July, 2015},
	location = {Madrid, Spain},
	month = {July},
}

@INPROCEEDINGS{350042,
	author={Thomas B{\"a}ck},
	booktitle={Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on},
	title={Selective pressure in evolutionary algorithms: a characterization of selection mechanisms},
	year={1994},
	month={Jun},
	pages={57-62 vol.1},
	keywords={genetic algorithms;probability;search problems;evolutionary algorithms;experimental investigation;exploration-exploitation tradeoff;genetic algorithm;linear ranking;proportional selection;search space;selection mechanisms;selection operators;selective pressure;simple unimodal objective function;theoretical results;tournament selection;Computer science;Electronic switching systems;Evolutionary computation;Genetic algorithms;Genetic mutations},
	doi={10.1109/ICEC.1994.350042},
}

@inproceedings{Blickle:1995:MAT:645514.658088,
	author = {Blickle, Tobias and Thiele, Lothar},
	title = {A Mathematical Analysis of Tournament Selection},
	booktitle = {Proceedings of the 6th International Conference on Genetic Algorithms},
	year = {1995},
	isbn = {1-55860-370-0},
	pages = {9--16},
	numpages = {8},
	url = {http://dl.acm.org/citation.cfm?id=645514.658088},
	acmid = {658088},
	publisher = {Morgan Kaufmann Publishers Inc.},
	address = {San Francisco, CA, USA},
}

@incollection{McPhee:2015:GPTP,
	author = {Nicholas Freitag McPhee and David Donatucci and Thomas Helmuth},
	title = {Using Graph Databases to Explore the Dynamics of Genetic Programming Runs},
	booktitle = {Genetic Programming Theory and Practice XIII},
	NOyear = {2015},
	NOeditor = {?},
	series = {Genetic and Evolutionary Computation},
	publisher = {Springer},
	pubstate = {In press}
}

@inproceedings{Starosta:1986:LPL:991365.991400,
	author = {Starosta, Stanley and Nomura, Hirosato},
	title = {Lexicase Parsing: A Lexicon-driven Approach to Syntactic Analysis},
	booktitle = {Proceedings of the 11th Coference on Computational Linguistics},
	series = {COLING '86},
	year = {1986},
	location = {Bonn, Germany},
	pages = {127--132},
	numpages = {6},
	url = {http://dx.doi.org/10.3115/991365.991400},
	doi = {10.3115/991365.991400},
	acmid = {991400},
	publisher = {Association for Computational Linguistics},
	address = {Stroudsburg, PA, USA},
}

@book{starosta1988case,
	title={The Case for Lexicase: An Outline of Lexicase Grammatical Theory},
	author={Starosta, S.},
	isbn={9780861876396},
	lccn={87032794},
	series={Open linguistics series},
	url={http://books.google.com/books?id=Tl9iAAAAMAAJ},
	year={1988},
	publisher={Pinter Publishers}
}

@InCollection{Krawiec:2015:GPTP,
	author =       "Krawiec, Krzysztof and Swan, Jerry and O'Reilly, Una-May",
	title =        "Behavioral Program Synthesis: Insights and Prospects",
	booktitle =    "Genetic Programming Theory and Practice XIII",
	year =         "2015",
	series =       "Genetic and Evolutionary Computation",
	publisher =    "Springer"
}

@InProceedings{Helmuth:2014:GECCO,
	author =	"Thomas Helmuth and Lee Spector",
	title =	"Word count as a traditional programming benchmark
	problem for genetic programming",
	booktitle =	"GECCO '14: Proceedings of the 2014 conference on
	Genetic and evolutionary computation",
	year = 	"2014",
	NOeditor =	"Christian Igel and Dirk V. Arnold and Christian Gagne
	and Elena Popovici and Anne Auger and Jaume Bacardit
	and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy
	Deb and Benjamin Doerr and James Foster and Tobias
	Glasmachers and Emma Hart and Malcolm I. Heywood and
	Hitoshi Iba and Christian Jacob and Thomas Jansen and
	Yaochu Jin and Marouane Kessentini and Joshua D.
	Knowles and William B. Langdon and Pedro Larranaga and
	Sean Luke and Gabriel Luque and John A. W. McCall and
	Marco A. {Montes de Oca} and Alison Motsinger-Reif and
	Yew Soon Ong and Michael Palmer and Konstantinos E.
	Parsopoulos and Guenther Raidl and Sebastian Risi and
	Guenther Ruhe and Tom Schaul and Thomas Schmickl and
	Bernhard Sendhoff and Kenneth O. Stanley and Thomas
	Stuetzle and Dirk Thierens and Julian Togelius and
	Carsten Witt and Christine Zarges",
	isbn13 =	"978-1-4503-2662-9",
	pages =	"919--926",
	keywords =	"genetic algorithms, genetic programming",
	month =	"12-16 " # jul,
	organisation = "SIGEVO",
	address =	"Vancouver, BC, Canada",
	URL =  	"http://doi.acm.org/10.1145/2576768.2598230",
	doi =  	"doi:10.1145/2576768.2598230",
	publisher =	"ACM",
	publisher_address = "New York, NY, USA",
	abstract =	"The Unix utility program wc, which stands for word
	count, takes any number of files and prints the number
	of newlines, words, and characters in each of the
	files. We show that genetic programming can find
	programs that replicate the core functionality of the
	wc utility, and propose this problem as a traditional
	programming benchmark for genetic programming systems.
	This wc problem features key elements of programming
	tasks that often confront human programmers, including
	requirements for multiple data types, a large
	instruction set, control flow, and multiple outputs.
	Furthermore, it mimics the behavior of a real-world
	utility program, showing that genetic programming can
	automatically synthesize programs with general utility.
	We suggest statistical procedures that should be used
	to compare performances of different systems on
	traditional programming problems such as the wc
	problem, and present the results of a short experiment
	using the problem. Finally, we give a short analysis of
	evolved solution programs, showing how they make use of
	traditional programming concepts.",
	notes =	"Also known as \cite{2598230} GECCO-2014 A joint
	meeting of the twenty third international conference on
	genetic algorithms (ICGA-2014) and the nineteenth
	annual genetic programming conference (GP-2014)",
	doi-url =	"http://dx.doi.org/10.1145/2576768.2598230",
}

@InProceedings{Krawiec:2015:GECCO:smgpWorkshop,
	author =	"Pawe{\l} Liskowski and Krzysztof Krawiec and Thomas Helmuth and Lee Spector",
	title =	"Comparison of Semantic-aware Selection Methods in Genetic Programming",
	booktitle =	"GECCO 2015 workshop on Semantic Methods in Genetic Programming",
	year = 	"2015",
	publisher =	"ACM",
}

@InProceedings{Helmuth:2013:GECCOcomp,
	author =	"Thomas Helmuth and Lee Spector",
	title =	"Evolving a digital multiplier with the pushgp genetic
	programming system",
	booktitle =	"GECCO '13 Companion: Proceeding of the fifteenth
	annual conference companion on Genetic and evolutionary
	computation conference companion",
	year = 	"2013",
	TooManyeditor =	"Christian Blum and Enrique Alba and Thomas
	Bartz-Beielstein and Daniele Loiacono and Francisco
	Luna and Joern Mehnen and Gabriela Ochoa and Mike
	Preuss and Emilia Tantar and Leonardo Vanneschi",
	isbn13 =	"978-1-4503-1964-5",
	keywords =	"genetic algorithms, genetic programming",
	pages =	"1627--1634",
	month =	"6-10 " # jul,
	organisation = "SIGEVO",
	address =	"Amsterdam, The Netherlands",
	doi =  	"doi:10.1145/2464576.2466814",
	publisher =	"ACM",
	publisher_address = "New York, NY, USA",
	abstract =	"A recent article on benchmark problems for genetic
	programming suggested that researchers focus attention
	on the digital multiplier problem, also known as the
	multiple output multiplier problem, in part because it
	is scalable and in part because the requirement of
	multiple outputs presents challenges for some forms of
	genetic programming [20]. Here we demonstrate the
	application of stack-based genetic programming to the
	digital multiplier problem using the PushGP genetic
	programming system, which evolves programs expressed in
	the stack-based Push programming language. We
	demonstrate the use of output instructions and argue
	that they provide a natural mechanism for producing
	multiple outputs in a stack-based genetic programming
	context. We also show how two recent developments in
	PushGP dramatically improve the performance of the
	system on the digital multiplier problem. These
	developments are the ULTRA genetic operator, which
	produces offspring via Uniform Linear Transformation
	with Repair and Alternation [12], and lexicase
	selection, which selects parents according to
	performance on cases considered sequentially in random
	order [11]. Our results using these techniques show not
	only their utility, but also the utility of the digital
	multiplier problem as a benchmark problem for genetic
	programming research. The results also demonstrate the
	exibility of stack-based genetic programming for
	solving problems with multiple outputs and for serving
	as a platform for experimentation with new genetic
	programming techniques.",
	notes =	"Also known as \cite{2466814} Distributed at
	GECCO-2013.",
	doi-url =	"http://dx.doi.org/10.1145/2464576.2466814",
}

@InCollection{Spector:2013:GPTP,
	author =	"Lee Spector and Thomas Helmuth",
	title =	"Uniform Linear Transformation with Repair and
	Alternation in Genetic Programming",
	booktitle =	"Genetic Programming Theory and Practice XI",
	year = 	"2013",
	series =	"Genetic and Evolutionary Computation",
	NOeditor =	"Rick Riolo and Jason H. Moore and Mark Kotanchek",
	publisher =	"Springer",
	chapter =	"8",
	pages =	"137--153",
	address =	"Ann Arbor, USA",
	month =	"9-11 " # may,
	keywords =	"genetic algorithms, genetic programming, Uniform
	mutation, Uniform crossover, ULTRA, Push, PushGP, Drug
	bioavailability problem, Pagie-1 problem, Factorial
	regression, Boolean multiplexer problem",
	isbn13 =	"978-1-4939-0374-0",
	doi =  	"doi:10.1007/978-1-4939-0375-7_8",
	abstract =	"Several genetic programming researchers have argued
	for the utility of genetic operators that act
	uniformly. By act uniformly we mean two specific
	things: that the probability of an inherited program
	component being modified during inheritance is
	independent of the size and shape of the parent
	programs beyond the component in question; and that
	pairs of parents are combined in ways that allow
	arbitrary combinations of components from each parent
	to appear in the child. Uniform operators described in
	previous work have had limited utility, however,
	because of a mismatch between the relevant notions of
	uniformity and the hierarchical structure and variable
	sizes of many genetic programming representations. In
	this chapter we describe a new genetic operator, ULTRA,
	which incorporates aspects of both mutation and
	crossover and acts approximately uniformly across
	programs of variable sizes and structures. ULTRA treats
	hierarchical programs as linear sequences and includes
	a repair step to ensure that syntax constraints are
	satisfied after variation. We show that on the drug
	bioavailability and Pagie-1 benchmark problems ULTRA
	produces significant improvements both in
	problem-solving power and in program size relative to
	standard operators. Experiments with factorial
	regression and with the Boolean 6-multiplexer problem
	demonstrate that ULTRA can manipulate programs that
	make use of hierarchical structure, but also that it is
	not always beneficial. The demonstrations evolve
	programs in the Push programming language, which makes
	repair particularly simple, but versions of the
	technique should be applicable in other genetic
	programming systems as well.",
	notes =	"http://cscs.umich.edu/gptp-workshops/ Part of
	\cite{Riolo:2013:GPTP} published after the workshop in
	2013",
	doi-url =	"http://dx.doi.org/10.1007/978-1-4939-0375-7_8",
}

@InProceedings{Spector:2012:GECCOcompANEW,
	author =	"Lee Spector",
	title =	"Assessment of Problem Modality by Differential
	Performance of Lexicase Selection in Genetic
	Programming: {A} Preliminary Report",
	booktitle =	"1st workshop on Understanding Problems (GECCO-UP)",
	year = 	"2012",
	NOeditor =	"Kent McClymont and Ed Keedwell",
	isbn13 =	"978-1-4503-1178-6",
	keywords =	"genetic algorithms, genetic programming, modal
	problems, selection, lexicase selection, problem
	metrics, modality",
	pages =	"401--408",
	month =	"7-11 " # jul,
	organisation = "SIGEVO",
	address =	"Philadelphia, Pennsylvania, USA",
	NOURL =  	"http://hampshire.edu/lspector/pubs/wk09p4-spector.pdf",
	doi =  	"doi:10.1145/2330784.2330846",
	publisher =	"ACM",
	publisher_address = "New York, NY, USA",
	size = 	"8 pages",
	abstract =	"Many potential target problems for genetic programming
	are modal in the sense that qualitatively different
	modes of response are required for inputs from
	different regions of the problem's domain. This paper
	presents a new approach to solving modal problems with
	genetic programming, using a simple and novel parent
	selection method called lexicase selection. It then
	shows how the differential performance of genetic
	programming with and without lexicase selection can be
	used to provide a measure of problem modality, and it
	argues that defining such a measure in this way is not
	as methodologically problematic as it may initially
	appear. The modality measure is illustrated through the
	analysis of genetic programming runs on a simple modal
	symbolic regression problem. This is a preliminary
	report that is intended in part to stimulate discussion
	on the significance of modal problems, methods for
	solving them, and methods for measuring the modality of
	problems. Although the core concepts in this paper are
	presented in the context of genetic programming, they
	are also relevant to applications of other forms of
	evolutionary computation to modal problems.",
	notes =	"p403 'allowing the high-performing individual [on any
	test case] to contribute to the following generation,
	regardless of [its] performance on other cases.' p405
	'with population size 10000 the overall cost of
	producing offspring using lexicase selection can exceed
	that of using ordinary tournaments by between one and
	two orders of magnitude'. (why?) Section 5: integer
	symbolic regresions with one, two or three 'modes'.
	Clojure lexicase.clj
	https://gist.github.com/lspector/2320883 Also known as
	\cite{2330846} Distributed at GECCO-2012. ACM Order
	Number 910122.",
	doi-url =	"http://dx.doi.org/10.1145/2330784.2330846",
}

@InProceedings{McKay:2000:GECCO,
	author =	"McKay, Robert I.",
	title =	"Fitness Sharing in Genetic Programming",
	pages =	"435--442",
	year = 	"2000",
	publisher =	"Morgan Kaufmann",
	booktitle =	"Proceedings of the Genetic and Evolutionary
	Computation Conference (GECCO-2000)",
	NOeditor =	"Darrell Whitley and David Goldberg and Erick Cantu-Paz
	and Lee Spector and Ian Parmee and Hans-Georg Beyer",
	address =	"Las Vegas, Nevada, USA",
	publisher_address = "San Francisco, CA 94104, USA",
	month =	"10-12 " # jul,
	keywords =	"genetic algorithms, genetic programming",
	ISBN = 	"1-55860-708-0",
	URL =  	"http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP256.pdf",
	URL =  	"http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP256.ps",
	notes =	"A joint meeting of the ninth International Conference
	on Genetic Algorithms (ICGA-2000) and the fifth Annual
	Genetic Programming Conference (GP-2000) Part of
	\cite{whitley:2000:GECCO}",
}

%%% Other selection techniques

@InProceedings{Fieldsend:2015:GECCO,
	author =	"Fieldsend, Jonathan E. and Moraglio, Alberto",
	title =	"Strength through diversity: Disaggregation and multi-objectivisation approaches for genetic programming",
	booktitle =	"GECCO '15: Proceedings of the 2015 conference on
	Genetic and evolutionary computation",
	year = 	"2015",
	publisher =	"ACM",
}

@InProceedings{Krawiec:ppsn2010,
	author =	"Krzysztof Krawiec and Pawel Lichocki",
	title =	"Using Co-solvability to Model and Exploit Synergetic
	Effects in Evolution",
	booktitle =	"PPSN 2010 11th International Conference on Parallel
	Problem Solving From Nature",
	pages =	"492--501",
	year = 	"2010",
	volume =	"6239",
	NOeditor =	"Robert Schaefer and Carlos Cotta and Joanna Kolodziej
	and Guenter Rudolph",
	publisher =	"Springer",
	series =	"Lecture Notes in Computer Science",
	isbn13 =	"978-3-642-15870-4",
	address =	"Krakow, Poland",
	month =	"11-15 " # sep,
	keywords =	"genetic algorithms, genetic programming",
	doi =  	"doi:10.1007/978-3-642-15871-1_50",
	abstract =	"We introduce, analyse, and experimentally examine
	co-solvability, an ability of a solution to solve a
	pair of fitness cases (tests). Based on this concept,
	we devise a co-solvability fitness function that makes
	solutions compete for rewards granted for solving pairs
	of tests, in a way analogous to implicit fitness
	sharing. We prove that co-solvability fitness function
	is by definition synergistic and imposes selection
	pressure which is qualitatively different from that of
	standard fitness function or implicit fitness sharing.
	The results of experimental verification on eight
	genetic programming tasks demonstrate that evolutionary
	runs driven by co-solvability fitness function usually
	converge faster to well-performing solutions and are
	more likely to reach global optima.",
	doi-url =	"http://dx.doi.org/10.1007/978-3-642-15871-1_50",
}

@InProceedings{Krawiec:2015:EuroGP,
	author =	"Krzysztof Krawiec and Pawel Liskowski",
	title =	"Automatic Derivation of Search Objectives for
	Test-Based Genetic Programming",
	booktitle =	"18th European Conference on Genetic Programming",
	year = 	"2015",
	NOeditor =	"Penousal Machado and Malcolm I. Heywood and James
	McDermott and Mauro Castelli and Pablo Garcia-Sanchez
	and Paolo Burelli and Sebastian Risi and Kevin Sim",
	series =	"LNCS",
	volume =	"9025",
	publisher =	"Springer",
	pages =	"53--65",
	address =	"Copenhagen",
	month =	"8-10 " # apr,
	organisation = "EvoStar",
	keywords =	"genetic algorithms, genetic programming, Program
	synthesis, Test-based problems, Multiobjective
	evolutionary computation",
	isbn13 =	"978-3-319-16500-4",
	doi =  	"doi:10.1007/978-3-319-16501-1_5",
	abstract =	"In genetic programming (GP), programs are usually
	evaluated by applying them to tests, and fitness
	function indicates only how many of them have been
	passed. We posit that scrutinising the outcomes of
	programs interactions with individual tests may help
	making program synthesis more effective. To this aim,
	we propose DOC, a method that autonomously derives new
	search objectives by clustering the outcomes of
	interactions between programs in the population and the
	tests. The derived objectives are subsequently used to
	drive the selection process in a single or
	multiobjective fashion. An extensive experimental
	assessment on 15 discrete program synthesis tasks
	representing two domains shows that DOC significantly
	outperforms conventional GP and implicit fitness
	sharing.",
	notes =	"Part of \cite{Machado:2015:GP} EuroGP'2015 held in
	conjunction with EvoCOP2015, EvoMusArt2015 and
	EvoApplications2015",
	doi-url =	"http://dx.doi.org/10.1007/978-3-319-16501-1_5",
}

@InProceedings{Galvan-Lopez:2013:CEC,
	article_id =	"1650",
	author =	"Edgar Galv{\'a}n-L{\'o}pez and Brendan Cody-Kenny and Leonardo
	Trujillo and Ahmed Kattan",
	title =	"Using Semantics in the Selection Mechanism in Genetic
	Programming: A Simple Method for Promoting Semantic
	Diversity",
	booktitle =	"2013 IEEE Conference on Evolutionary Computation",
	volume =	"1",
	year = 	"2013",
	month =	jun # " 20-23",
	editor =	"Luis Gerardo {de la Fraga}",
	pages =	"2972--2979",
	address =	"Cancun, Mexico",
	keywords =	"genetic algorithms, genetic programming",
	isbn13 =	"978-1-4799-0453-2",
	doi =  	"doi:10.1109/CEC.2013.6557931",
	notes =	"CEC 2013 - A joint meeting of the IEEE, the EPS and
	the IET.",
	doi-url =	"http://dx.doi.org/10.1109/CEC.2013.6557931",
}

@Article{spector:2002:GPEM,
	author =       "Lee Spector and Alan Robinson",
	title =        "Genetic Programming and Autoconstructive Evolution
	with the Push Programming Language",
	journal =      "Genetic Programming and Evolvable Machines",
	year =         "2002",
	volume =       "3",
	number =       "1",
	pages =        "7--40",
	month =        mar,
	keywords =     "genetic algorithms, genetic programming, artificial
	life, modularity, programming languages,
	self-adaptation",
	ISSN =         "1389-2576",
	URL =          "http://hampshire.edu/lspector/pubs/push-gpem-final.pdf",
	doi =          "doi:10.1023/A:1014538503543",
	abstract =     "Push is a programming language designed for the
	expression of evolving programs within an evolutionary
	computation system. This article describes Push and
	illustrates some of the opportunities that it presents
	for evolutionary computation. Two evolutionary
	computation systems, PushGP and Pushpop, are described
	in detail. PushGP is a genetic programming system that
	evolves Push programs to solve computational problems.
	Pushpop, an ?autoconstructive evolution? system, also
	evolves Push programs but does so while simultaneously
	evolving its own evolutionary mechanisms.",
	notes =        "Article ID: 395988",
}

@InProceedings{1068292,
	author =       "Lee Spector and Jon Klein and Maarten Keijzer",
	title =        "The {Push3} execution stack and the evolution of
	control",
	booktitle =    "{GECCO 2005}: Proceedings of the 2005 conference on
	Genetic and evolutionary computation",
	year =         "2005",
	ISBN =         "1-59593-010-8",
	pages =        "1689--1696",
	address =      "Washington DC, USA",
	URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1689.pdf",
	doi =          "doi:10.1145/1068009.1068292",
	publisher =    "ACM Press",
	publisher_address = "New York, NY, 10286-1405, USA",
	organisation = "ACM SIGEVO (formerly ISGEC)",
	keywords =     "genetic algorithms, genetic programming, combinators,
	experimentation, exponentiation, factorial, Fibonacci
	sequence, iteration, languages, parity, push,
	recursion, reversing a list, sorting, stack-based
	genetic programming",
	abstract =     "One of Push's attractive features in this context is
	its transparent support for the expression and
	evolution of modular architectures and complex control
	structures, achieved through explicit code
	self-manipulation. The latest version of Push, Push3,
	enhances this feature by permitting explicit
	manipulation of an execution stack that contains the
	expressions that are queued for execution in the
	interpreter. presents a series of examples in which
	Push3 was used with a simple genetic programming system
	(PushGP) to evolve programs with non-trivial control
	structures.",
	notes =        "GECCO-2005 A joint meeting of the fourteenth
	international conference on genetic algorithms
	(ICGA-2005) and the tenth annual genetic programming
	conference (GP-2005).

	ACM Order Number 910052

	Cited by \cite{eurogp06:AgapitosLucas}

	EXEC stack. Combinators. Reentrant p1691 'the
	re-entrant interpreter allows for the controlled
	execution of a particular number of instructions per
	time-step.' Reversing a list. Factorial. Fibonacci.
	Parity. Powers of two, integer exponentiation
	Expt(2,n). Sorting a list.",
}

@book{koza1992genetic,
	title={Genetic programming: on the programming of computers by means of natural selection},
	author={Koza, John R},
	volume={1},
	year={1992},
	publisher={MIT press}
}

@inproceedings{gonccalves2015generalization,
	title={On the generalization ability of geometric semantic genetic programming},
	author={Gon{\c{c}}alves, Ivo and Silva, Sara and Fonseca, Carlos M},
	booktitle={European Conference on Genetic Programming},
	pages={41--52},
	year={2015},
	organization={Springer}
}

@InCollection{Helmuth:2016:GPTP,
	author =       "Helmuth, Thomas and Spector, Lee and McPhee, Nicholas Freitag and Shanabrook, Saul",
	title =        "Linear genomes for structured programs",
	booktitle =    "Genetic Programming Theory and Practice XIV",
	year =         "2016",
	series =       "Genetic and Evolutionary Computation",
	publisher =    "Springer"
}

@InProceedings{Spector:2014:GECCOcomp,
	author =	"Lee Spector and Thomas Helmuth",
	title =	"Effective simplification of evolved push programs
	using a simple, stochastic hill-climber",
	booktitle =	"GECCO Comp '14: Proceedings of the 2014 conference
	companion on Genetic and evolutionary computation
	companion",
	year = 	"2014",
	NOeditor =	"Christian Igel and Dirk V. Arnold and Christian Gagne
	and Elena Popovici and Anne Auger and Jaume Bacardit
	and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy
	Deb and Benjamin Doerr and James Foster and Tobias
	Glasmachers and Emma Hart and Malcolm I. Heywood and
	Hitoshi Iba and Christian Jacob and Thomas Jansen and
	Yaochu Jin and Marouane Kessentini and Joshua D.
	Knowles and William B. Langdon and Pedro Larranaga and
	Sean Luke and Gabriel Luque and John A. W. McCall and
	Marco A. {Montes de Oca} and Alison Motsinger-Reif and
	Yew Soon Ong and Michael Palmer and Konstantinos E.
	Parsopoulos and Guenther Raidl and Sebastian Risi and
	Guenther Ruhe and Tom Schaul and Thomas Schmickl and
	Bernhard Sendhoff and Kenneth O. Stanley and Thomas
	Stuetzle and Dirk Thierens and Julian Togelius and
	Carsten Witt and Christine Zarges",
	isbn13 =	"978-1-4503-2881-4",
	keywords =	"genetic algorithms, genetic programming: Poster",
	pages =	"147--148",
	month =	"12-16 " # jul,
	organisation = "SIGEVO",
	address =	"Vancouver, BC, Canada",
	URL =  	"http://doi.acm.org/10.1145/2598394.2598414",
	doi =  	"doi:10.1145/2598394.2598414",
	publisher =	"ACM",
	publisher_address = "New York, NY, USA",
	abstract =	"Genetic programming systems often produce programs
	that include unnecessary code. This is undesirable for
	several reasons, including the burdens that
	overly-large programs put on end-users for program
	interpretation and maintenance. The problem is
	exacerbated by recently developed techniques, such as
	genetic programming with geometric semantic crossover,
	that tend to produce enormous programs. Methods for
	automatically simplifying evolved programs are
	therefore of interest, but automatic simplification is
	non-trivial in the context of traditional program
	representations with unconstrained function sets. Here
	we show how evolved programs expressed in the
	stack-based Push programming language can be
	automatically and reliably simplified using a simple,
	stochastic hill-climber. We demonstrate and
	quantitatively characterise this simplification process
	on programs evolved to solve four non-trivial genetic
	programming problems with qualitatively different
	function sets.",
	notes =	"Also known as \cite{2598414} Distributed at
	GECCO-2014.",
	doi-url =	"http://dx.doi.org/10.1145/2598394.2598414",
}

@InProceedings{hooper:1996:iarGPes,
	author =	"Dale Hooper and Nicholas S. Flann",
	title =	"Improving the Accuracy and Robustness of Genetic
	Programming through Expression Simplification",
	booktitle =	"Genetic Programming 1996: Proceedings of the First
	Annual Conference",
	NOeditor =	"John R. Koza and David E. Goldberg and David B. Fogel
	and Rick L. Riolo",
	year = 	"1996",
	month =	"28--31 " # jul,
	keywords =	"genetic algorithms, genetic programming",
	pages =	"428",
	address =	"Stanford University, CA, USA",
	publisher =	"MIT Press",
	ISBN = 	"0-262-61127-9",
	URL =  	"http://digital.cs.usu.edu/~flann/gp.pdf",
	URL =  	"http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap67.pdf",
	URL =  	"http://cognet.mit.edu/library/books/view?isbn=0262611279",
	size = 	"1 page",
	notes =	"GP-96. Occam's razor, bloat, introns, 200 edit rules",
}

@article{iba1994genetic,
	title={Genetic programming using a minimum description length principle},
	author={Iba, Hitoshi and De Garis, Hugo and Sato, Taisuke},
	journal={Advances in genetic programming},
	volume={1},
	pages={265--284},
	year={1994}
}

extension: bib


text:
@article{Bosch2016Oct,
	author = {Bosch, S. E. and Seeliger, K. and van Gerven, M. A. J.},
	title = {{Modeling Cognitive Processes with Neural Reinforcement Learning}},
	journal = {bioRxiv},
	pages = {084111},
	year = {2016},
	month = {Oct},
	publisher = {Cold Spring Harbor Laboratory},
	eprint = {084111},
	doi = {10.1101/084111}
}

repository: sebosch/website

repo_name: sebosch/website