%Original file available from http://www.cs.odu.edu/~jbollen/bibliographies/bibtex/CS_alg.bib
%Last update: Tuesday 16 August 2005
%Current number of entries: 10

@article{inside:bianchini2005,
 author = {Monica Bianchini and Marco Gori and Franco Scarselli},
 title = {Inside PageRank},
 journal = {ACM Trans. Inter. Tech.},
 volume = {5},
 number = {1},
 year = {2005},
 issn = {1533-5399},
 pages = {92--128},
 doi = {http://doi.acm.org/10.1145/1052934.1052938},
 publisher = {ACM Press},
 address = {New York, NY, USA},
 }


@ARTICLE{usegen:dunn1998,
title = {The use of genetic algorithms and stochastic hill-climbing in
               dynamic finite element model identification},
author = {S. A. Dunn},
year = 1998,
journal = {Computers \& Structures},
volume = {66},
number = 4,
pages = {489 -- 497},
month = {February},
}



@ARTICLE{palopr:greiner1996,
title = {PALO: a probabilistic hill-climbing algorithm},
author = {Russel Greiner},
year = 1996,
journal = {Artificial Intelligence},
volume = {84},
number = {1-2},
pages = {177 -- 208},
abstract = {Many learning systems search through a space of possible performance elements,
     seeking an element whose expected utility, over the distribution of problems, is
     high. As the task of finding the globally optimal element is often intractable, many
     practical learning systems instead hill-climb to a local optimum. Unfortunately,
     even this is problematic as the learner typically does not know the underlying
     distribution of problems, which it needs to determine an element's expected utility.
     This paper addresses the task of approximating this hill-climbing search when the
     utility function can only be estimated by sampling. We present a general algorithm,
     palo, that returns an element that is, with provably high probability, essentially a
     local optimum. We then demonstrate the generality of this algorithm by presenting
     three distinct applications that respectively find an element whose efficiency,
     accuracy or completeness is nearly optimal. These results suggest approaches to
     solving the utility problem from explanation-based learning, the multiple extension
     problem from nonmonotonic reasoning and the tractability/completeness tradeoff
     problem from knowledge representation.},
}



@ARTICLE{compar:polgar2000,
author = {O. Polg\'{a}r and M. Fried and T. Lohner and I. B\'{a}rsony},
title = {Comparison of algorithms used for evaluation of ellipsometric
               measurements random search, genetic algorithms, simulated annealing
               and hill climbing graph-searches},
year = 2000,
journal = {Surface Science},
volume = 457,
number = {1-2},
month = 1,
pages = {157 -- 177},
}

@ARTICLE{ants:wagner2000,
title = {{ANTS}: Agents on Networks, Trees, and Subgraphs},
author = {Israel A. Wagner and Michael Lindenbaum and Alfred M. Bruckstein},
year = 2000,
journal = {Future Generation Computer Systems},
volume = 16,
pages = {915--926},
number = 8,
}



@BOOK{introd:cormen1997,
author = {Thomas H. Cormen and Charles E. Leiserson and Ronald
                  L. Rivest},
title = {Introduction to Algorithms},
year = 1997,
publisher = {The MIT press},
address = {Cambridge, Massachusetts}
}


@BOOK{cluste:spaeth1980,
title = {Cluster analysis algorithms},
author = {Helmuth Spath},
year = {1980},
publisher = {Ellis Horwood},
address = {Chichester, UK},
}



@BOOK{cluste:everitt1974,
title = {Cluster analysis},
author = {Brian Everitt},
year = {1974},
publisher = {Halsted Press},
address = {New York},
}



@BOOK{swarmi:bonabeau1999,
title = {Swarm intelligence : from natural to artificial systems},
author = {Eric Bonabeau and Marco Dorigo and Guy Theraulaz},
publisher = {Oxford University Press},
year = 1999,
address = {New York},
}



@INPROCEEDINGS{distri:colorni1992,
author = {A. Colorni and M. Dorigo and V. Maniezzo},
title = {Distributed optimization by ant colonies},
year = 1992,
booktitle = {Proceedings of the {F}irst {E}uropean {C}onference on {A}rtificial {L}ife},
address = {Paris, France},
pages = {134 -- 142},
}



@ARTICLE{evolvi:botee1999,
author = {H.M. Botee and E. Bonabeau},
title = {Evolving ant colony optimization},
journal = {Advances in Complex Systems},
volume = 1,
number = {2-3},
pages = {149 -- 159},
year = 1999,
}