@article{woodward_divergent_2003, title = {Divergent narratives in the imagining of the home amongst middle-class consumers - Aesthetics, comfort and the symbolic boundaries of self and home}, author = {Ian Woodward}, journal = {The Australian and New Zealand journal of sociology}, pages = {391-412}, url = {http://x-port-sfx.uio.no/sfx\_ubit?url\_ver=Z39.88-2004;url\_ctx\_fmt=info\%3Aofi\%2Ffmt\%3Akev\%3Amtx\%3Actx;rft\_val\_fmt=info\%3Aofi\%2Ffmt\%3Akev\%3Amtx\%3Ajournal;rft.atitle=Divergent\%20narratives\%20in\%20the\%20imagining\%20of\%20the\%20home\%20amongst\%20middle-class\%20consumers\%20-\%20Aesthetics\%2C\%20comfort\%20and\%20the\%20symbolic\%20boundaries\%20of\%20self\%20and\%20home;rft.auinit=I;rft.aulast=Woodward;rft.date=2003;rft.epage=412;rft.genre=article;rft.issn=0004-8690;rft.issue=4;rft.spage=391;rft.stitle=J\%20SOCIOL;rft.title=JOURNAL\%20OF\%20SOCIOLOGY;rft.volume=39;rfr\_id=info\%3Asid\%2Fwww.isinet.com\%3AWoK\%3AWOS}, volume = {39}, year = {2003}, biburl = {http://www.bibsonomy.org/bibtex/259d726ffa7705dbb5724b06fc86f60ba/snauth}, issn = {0004-8690}, keywords = {imported } } @article{woodward_domestic_2001, title = {Domestic objects and the taste epiphany - A resource for consumption methodology}, author = {Ian Woodward}, journal = {Journal of material culture}, pages = {115-136}, url = {http://x-port-sfx.uio.no/sfx\_ubit?url\_ver=Z39.88-2004;url\_ctx\_fmt=info\%3Aofi\%2Ffmt\%3Akev\%3Amtx\%3Actx;rft\_val\_fmt=info\%3Aofi\%2Ffmt\%3Akev\%3Amtx\%3Ajournal;rft.atitle=Domestic\%20objects\%20and\%20the\%20taste\%20epiphany\%20-\%20A\%20resource\%20for\%20consumption\%20methodology;rft.auinit=I;rft.aulast=Woodward;rft.date=2001;rft.epage=136;rft.genre=article;rft.issn=1359-1835;rft.issue=2;rft.spage=115;rft.stitle=J\%20MAT\%20CULT;rft.title=JOURNAL\%20OF\%20MATERIAL\%20CULTURE;rft.volume=6;rfr\_id=info\%3Asid\%2Fwww.isinet.com\%3AWoK\%3AWOS}, volume = {6}, year = {2001}, biburl = {http://www.bibsonomy.org/bibtex/233dba4ab5e21bc7516f1035288453e28/snauth}, abstract = {The presentation of an aesthetic identity involves the accomplishment of a coherent, plausible narrative which links one's choices to desired characteristics of the self. As symbolic evidence of a person's taste, material culture is a vital component of a successful narrative. Via case studies of pivotal household objects, this paper uses face-to-face interview data as a way of investigating processes of aesthetic choice. Household objects are interpreted as material elements imbricated in the presentation of a socially plausible and internally consistent aesthetic self. Narrative analysis, and the concept of the epiphany-object, are proposed as useful ways of accounting for tastes in domestic material culture. Methodological questions of truth-telling and authenticity in the face-to-face context are considered, and the sociological problem of taste is scrutinized in light of ideas about social accountability and textual identity.}, issn = {1359-1835}, keywords = {imported } } @inproceedings{conf/mva/WoodwardD05, title = {Combining Computer Graphics and Image Processing for Low Cost Realistic 3D Face Generation and Animation.}, author = {Alexander Woodward and Patrice Delmas}, booktitle = {MVA}, crossref = {conf/mva/2005}, pages = {120-123}, url = {http://dblp.uni-trier.de/db/conf/mva/mva2005.html#WoodwardD05}, year = {2005}, biburl = {http://www.bibsonomy.org/bibtex/2c2647f1e596a5ff91363a101d9de8e39/dblp}, description = {dblp}, ee = {http://b2.cvl.iis.u-tokyo.ac.jp/mva/proceedings/CommemorativeDVD/2005/papers/2005120.pdf}, isbn = {4-901122-04-5}, date = {2008-06-30}, keywords = {dblp } } @inproceedings{conf/cbms/RandellWWG08, title = {Public Yet Private: The Status, Durability and Visibility of Handover Sheets.}, author = {Rebecca Randell and Peter Woodward and Stephanie Wilson and Julia Galliers}, booktitle = {CBMS}, crossref = {conf/cbms/2008}, pages = {500-502}, publisher = {IEEE Computer Society}, url = {http://dblp.uni-trier.de/db/conf/cbms/cbms2008.html#RandellWWG08}, year = {2008}, biburl = {http://www.bibsonomy.org/bibtex/25e82351c6c5ce0fa27549e93924d2a4f/dblp}, description = {dblp}, ee = {http://doi.ieeecomputersociety.org/10.1109/CBMS.2008.52}, isbn = {978-0-7695-3165-6}, date = {2008-06-24}, keywords = {dblp } } @inproceedings{eurogp06:Woodward, title = {Invariance of Function Complexity under Primitive Recursive Functions}, address = {Budapest, Hungary}, author = {John R. Woodward}, booktitle = {Proceedings of the 9th European Conference on Genetic Programming}, editor = {Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art}, month = {10 - 12 April}, pages = {310--319}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, url = {http://link.springer.de/link/service/series/0558/papers/3905/39050310.pdf}, volume = {3905}, year = {2006}, biburl = {http://www.bibsonomy.org/bibtex/25d3dc09f603b61d926b921578a9e02c8/brazovayeye}, abstract = {Genetic Programming (GP) \cite{banzhaf:1997:book} often uses a tree form of a graph to represent solutions. An extension to this representation, Automatically Defined Functions (ADFs) is to allow the ability to express modules. In Woodward we proved that the complexity of a function is independent of the primitive set (function set and terminal set) if the representation has the ability to express modules. This is essentially due to the fact that if a representation can express modules, then it can effectively define its own primitives at a constant cost. This is reminiscent of the result that the complexity of a bit string is independent of the choice of Universal Turing Machine (UTM) (within an additive constant), the constant depending on the UTM but not on the function. The representations typically used in GP are not capable of expressing recursion, however a few researchers have introduced recursion into their representations. These representations are then capable of expressing a wider classes of functions, for example the primitive recursive functions (PRFs). We prove that given two representations which express the PRFs (and only the PRFs), the complexity of a function with respect to either of these representations is invariant within an additive constant. This is in the same vein as the proof of the invariants of Kolmogorov complexity and the proof in Woodward.}, bibsource = {DBLP, http://dblp.uni-trier.de}, organisation = {EvoNet}, isbn = {3-540-33143-3}, notes = {Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006}, keywords = {algorithms, genetic programming } } @inproceedings{eurogp06:WoodwardC, title = {Complexity and Cartesian Genetic Programming}, address = {Budapest, Hungary}, author = {John R. Woodward}, booktitle = {Proceedings of the 9th European Conference on Genetic Programming}, editor = {Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art}, month = {10 - 12 April}, pages = {260--269}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, url = {http://link.springer.de/link/service/series/0558/papers/3905/39050260.pdf}, volume = {3905}, year = {2006}, biburl = {http://www.bibsonomy.org/bibtex/21277da3a7fd282e29688a50742921db3/brazovayeye}, abstract = {Genetic Programming (GP) \cite{banzhaf:1997:book} often uses a tree form of a graph to represent solutions. An extension to this representation, Automatically Defined Functions (ADFs) is to allow the ability to express modules. In Woodward we proved that the complexity of a function is independent of the primitive set (function set and terminal set) if the representation has the ability to express modules. This is essentially due to the fact that if a representation can express modules, then it can effectively define its own primitives at a constant cost. Cartesian Genetic Programming (CGP) is a relative new type of representation used in Evolutionary Computation (EC), and differs from the tree based representation in that outputs from previous computations can be reused. This is achieved by representing programs as directed acyclic graphs (DAGs), rather than as trees. Thus computations from subtrees can be reused to reduce the complexity of a function. We prove an analogous result to that in Woodward the complexity of a function using a (Cartesian Program) CP representation is independent of the terminal set (up to an additive constant), provided the different terminal sets can both be simulated. This is essentially due to the fact that if a representation can express Automatic Reused Outputs \cite{miller:2000:CGP}, then it can effectively define its own terminals at a constant cost.}, bibsource = {DBLP, http://dblp.uni-trier.de}, organisation = {EvoNet}, isbn = {3-540-33143-3}, notes = {Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006}, keywords = {Cartesian Genetic Programming algorithms, genetic programming, } } @phdthesis{woodward:thesis, title = {Algorithm Induction, Modularity and Complexity}, address = {UK}, author = {John R. Woodward}, school = {School of Computer Science, The University of Birmingham}, year = {2005}, biburl = {http://www.bibsonomy.org/bibtex/2cef5208f5f2cee8553ab7cc8f92ad5b2/brazovayeye}, abstract = {We are concerned with the induction of a rule from a set of observations, the goal being to succinctly describe the observed data, but more importantly to place us in a position to make predictions about future data which we have not previously seen. One approach to rule induction is to form a hypothesis space which consists of potential rules or hypotheses, and then search this space for a rule which is consistent with the observed data. One formulation of this approach is Genetic Programming, where the hypothesis spaces consists of computer programs and the search of this space is conducted using biologically inspired search operators and a fitness function. The well known No Free Lunch theorems are central to search and essentially says all search algorithms perform equally, under a number of assumptions. We examine these assumptions and show that they are invalidated when the hypothesis space contains hypotheses which represent functions with different frequencies, as is the case with Genetic Programming and a number of other Machine Learning paradigms. The Conservation of Generalisation, which is related to the No Free Lunch theorems, implies that generalisation is impossible. This is contrary to Occam's razor. The Conservation of Generalisation theorems and Occam's razor are consistent if we restrict ourselves to representations which do not compress the observed data. We define the representational complexity of a function to be the minimum size of a given representation which can express the function. Given a primitive set, and the operation of composition, new functions can be constructed, which is the representation standard Genetic Programming uses to express functions. However the complexity of a function under this type of representation will depend on the primitive set. We prove that if a representation is capable of expressing modules (i.e. reuse of component parts of the representation), then the complexity of a function is independent of the primitive set (up to a constant which depends on the primitive set but not on the function being represented). We then conduct a number of experiments related to the evolution of Turing Complete representations. We argue that, if a representation can address the general case of a variable sized problem then the average number of evaluations to find a solution is independent of the problem size. In Genetic Programming, a fitness function is used to drive the evolutionary process and promote programs which are more likely to lead to solutions. We experiment with a fitness function which includes information about whether or not a program had to be aborted during it's evaluation and demonstrate that a 10 fold reduction in the number of evaluations can be achieved on an arithmetic problem. Finally, we experiment with a crossover operator which is inspired by the theory of recursive functions and reduced the probability of producing a program which has to be terminated during its evaluation.}, email = {jrw@cs.bham.ac.uk}, keywords = {algorithms, genetic programming } } @inproceedings{woodward:2005:UKCIb, title = {Invariance of Function Complexity under Primitive Recursive Functions}, address = {London}, author = {John R. Woodward}, booktitle = {The 5th annual UK Workshop on Computational Intelligence}, editor = {Boris Mirkin and George Magoulas}, month = {5-7 September}, pages = {281--288}, url = {http://www.dcs.bbk.ac.uk/ukci05/ukci05proceedings.pdf}, year = {2005}, biburl = {http://www.bibsonomy.org/bibtex/29a7b40a0332e086f047b9daf7704706e/brazovayeye}, abstract = {Genetic Programming (GP) \cite{banzhaf:1997:book} often uses a tree form of a graph to represent solutions in a search space. An extension to this representation, Automatically Defined Functions (ADFs) \cite{banzhaf:1997:book} is to allow the ability to express modules. In \cite{woodward:Modularity} we proved that the complexity of a function is independent of the primitive set (function set and terminal set) if the representation has the ability to express modules. This is essentially due to the fact that if a representation can express modules, then it can effectively define its own primitives at a constant cost. This is analogous to the to the result that the complexity of a bit string is independent of the Universal Turing Machine (UTM) (within an additive constant) \cite{ming93introduction}. The constant depending on the UTM but not on the function. The representations typically used in GP are not capable of expressing recursion, however a few researchers have introduced recursion into the representation. These representations are then capable of expressing the primitive recursive functions (PRFs) which are a subclass of the partial recursive function (which correspond to the computable functions). We prove that given two representations which express the PRFs, the complexity of a function with respect to either of these representations is invariant within an additive constant. This is in the same vein as the proof of the invariants of Kolmogorov complexity \cite{ming93introduction} and the proof in \cite{woodward:Modularity}. We comment on the important of the class of PRFs for learning.}, size = {8 pages}, email = {jrw@cs.bham.ac.uk}, notes = {UKCI 2005 http://www.dcs.bbk.ac.uk/ukci05/}, keywords = {ADF algorithms, genetic programming, } } @inproceedings{woodward:2005:UKCIa, title = {Complexity and Cartesian Genetic Programming}, address = {London}, author = {John R. Woodward}, booktitle = {The 5th annual UK Workshop on Computational Intelligence}, editor = {Boris Mirkin and George Magoulas}, month = {5-7 September}, pages = {273--280}, year = {2005}, biburl = {http://www.bibsonomy.org/bibtex/292975fa0c8b97de4c696bde70b6378f2/brazovayeye}, abstract = {Genetic Programming (GP) \cite{banzhaf:1997:book} often uses a tree form of a graph to represent solutions in a search space. An extension to this representation, Automatically Defined Functions (ADFs) \cite{banzhaf:1997:book} is to allow the ability to express modules. In \cite{woodward:Modularity} we proved that the complexity of a function is independent of the primitive sets (function set and terminal set) if the representation has the ability to express modules. This is essentially due to the fact that if a representation can express modules, then it can effectively define its own primitives at a constant cost. Cartesian Genetic Programming (CGP) \cite{miller:2000:CGP} is a relative new type of representation used in Evolutionary Computation (EC), and differs from the tree based representation in that outputs from previous computations can be reused. This is achieved by representing programs as directed acyclic graphs (DAGs), rather than as trees. Thus computations from subtrees can be reused to reduce the complexity of a function. We prove an analogous result to that in \cite{woodward:Modularity}; the complexity of a function using a (Cartesian Program) CP representation is invariant (up to an additive constant) when using different terminal sets, provided the different terminal sets can both be represented. This is essentially due to the fact that if a representation can express Automatic Reused Outputs \cite{miller:2000:CGP}, then it can effectively define its own terminals at a constant cost.}, notonline = {http://www.cs.bham.ac.uk/~jrw/publications/}, size = {8 pages}, email = {jrw@cs.bham.ac.uk}, notes = {UKCI 2005 http://www.dcs.bbk.ac.uk/ukci05/}, keywords = {Cartesian Genetic Programming algorithms, genetic programming, } } @inproceedings{Burke:2007:cec, title = {The Scalability of Evolved on Line Bin Packing Heuristics}, address = {Singapore}, author = {E. K. Burke and M. R. Hyde and G. Kendall and J. R. Woodward}, booktitle = {2007 IEEE Congress on Evolutionary Computation}, editor = {Dipti Srinivasan and Lipo Wang}, month = {25-28 September}, organization = {IEEE Computational Intelligence Society}, pages = {2530--2537}, publisher = {IEEE Press}, year = {2007}, biburl = {http://www.bibsonomy.org/bibtex/2c0d9f09b19e8130d9f0bdb0084402bd1/brazovayeye}, abstract = {The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed 'bestfit' algorithm. Here we examine the performance of the evolved heuristics on larger instances of the problem, which contain many more pieces than the problem instances used in training. In previous work, we concluded that we could confidently apply our heuristics to new instances of the same class of problem. Now we can make the additional claim that we can confidently apply our heuristics to problems of much larger size, not only without deterioration of solution quality, but also within a constant factor of the performance obtained by 'best fit'. Interestingly, our evolved heuristics respond to the number of pieces in a problem instance although they have no explicit access to that information. We also comment on the important point that, when solutions are explicitly constructed for single problem instances, the size of the search space explodes. How- ever, when working in the space of algorithmic heuristics, the distribution of functions represented in the search space reaches some limiting distribution and therefore the combinatorial explosion can be controlled.}, file = {1668.pdf}, isbn = {1-4244-1340-0}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, keywords = {algorithms, genetic programming } }