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fixed typos thanks to Diego Noble

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1 parent 2d20ad6 commit 7ecfe51c9bac7bf48c219f51020d461fe8be9d17 @jbrownlee committed Feb 2, 2012
Showing with 79 additions and 69 deletions.
  1. +3 −0 book/b_errata.tex
  2. +2 −2 book/c_advanced/racing_algorithms.tex
  3. +74 −67 workspace/bibtex.bib
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@@ -21,9 +21,12 @@ \section*{First Edition, Revision 1}
\item[page 129] Bug in the \texttt{one\_point\_crossover} function of the Grammatical Evolution implementation. Thanks to Mark Chenoweth.
\item[page 234] Fixed ambiguous pseudo code description of Particle Swarm Optimization. Thanks to Stefan Pauleweit.
\item[page 235] Fixed a bug in the \texttt{get\_global\_best} function of the Particle Swarm Optimization implementation. Thanks to Paul Chinnery.
+ \item[page 237] Improved reference [3] for Particle Swarm Optimization. Thanks to Diego Noble.
\item[page 242] Fixed a bug in the \texttt{search} function of the Ant System implementation. Thanks to Andrew Myers.
\item[page 330] Typo in taxonomy of LVQ algorithm. Thanks to Jason Davies.
\item[page 393] Typo in "Function Approximation" section. Thanks to Diego Noble.
+ \item[page 400] Typo in subsection 9.6.1. Thanks to Diego Noble.
+ \item[page 402] Typo in subsection 9.6.2. Thanks to Diego Noble.
\item[page 415] Changed equality to assignment in Ruby flow control example in Appendix A. Thanks to Donald Doherty.
\item[page 413] Typo in Overview section in Appendix A. Thanks to Martin-Louis Bright.
\item[page 413] Typo in Ruby Files section in Appendix A. Thanks to Brook Tamir.
@@ -18,7 +18,7 @@ \subsection{Issues of Benchmarking Methodology}
\index{Benchmarking!Issues}
Empirically comparing the performance of algorithms on optimization problem instances is a staple for the fields of Heuristics and Biologically Inspired Computation, and the problems of effective comparison methodology have been discussed since the inception of these fields. Johnson suggests that the coding of an algorithm is the easy part of the process; the difficult work is getting meaningful and publishable results \cite{Johnson2002a}. He goes on to provide a very through list of questions to consider before racing algorithms, as well as what he describes as his ``pet peeves'' within the field of empirical algorithm research.
-Hooker \cite{Hooker1995} (among others) practically condemns what he refers to as competitive testing of heuristic algorithms, calling it ``\emph{fundamentally anti-intellectual}''. He goes on to strongly encourag a rigorous methodology of what he refers to as scientific testing where the aim is to investigate algorithmic behaviors.
+Hooker \cite{Hooker1995} (among others) practically condemns what he refers to as competitive testing of heuristic algorithms, calling it ``\emph{fundamentally anti-intellectual}''. He goes on to strongly encourage a rigorous methodology of what he refers to as scientific testing where the aim is to investigate algorithmic behaviors.
Barr, Golden et~al.\ \cite{Barr1995} list a number of properties worthy of a heuristic method making a contribution, which can be paraphrased as; efficiency, efficacy, robustness, complexity, impact, generalizability, and innovation. This is interesting given that many (perhaps a majority) of conference papers focus on solution quality alone (one aspect of efficacy).
In their classical work on reporting empirical results of heuristics Barr, Golden et~al.\ specify a loose experimental setup methodology with the following steps:
@@ -49,7 +49,7 @@ \subsection{Selecting Algorithm Parameters}
There are many solutions to this problem such as self-adaptive parameters, meta-algorithms (for searching for good parameter values), and methods of performing sensitivity analysis over parameter ranges. A good introduction to the parameterization of genetic algorithms is Lobo, Lima et~al.\ \cite{Lobo2007}. The best and self-evident place to start (although often ignored \cite{Eiben2002}) is to investigate the literature and see what parameters been used historically. Although not a robust solution, it may prove to be a useful starting point for further investigation. The traditional approach is to run an algorithm on a large number of test instances and generalize the results \cite{Schaffer1989}. We, as a field, haven't really come much further than this historical methodology other than perhaps the application of more and differing statistical methods to decrease effort and better support findings.
-A promising area of study involves treating the algorithm as a complex systems, where problem instances may become yet another parameter of the model \cite{Saltelli2002, Campolongo2000}. From here, sensitivity analysis can be performed in conjunction with statistical methods to discover parameters that have the greatest effect \cite{Chan1997} and perhaps generalize model behaviors.
+A promising area of study involves treating the algorithm as a complex system, where problem instances may become yet another parameter of the model \cite{Saltelli2002, Campolongo2000}. From here, sensitivity analysis can be performed in conjunction with statistical methods to discover parameters that have the greatest effect \cite{Chan1997} and perhaps generalize model behaviors.
Francois and Lavergne \cite{Francois2001} mention the deficiencies of the traditional trial-and-error and experienced-practitioner approaches to parameter tuning, further suggesting that seeking general rules for parameterization will lead to optimization algorithms that offer neither convergent or efficient behaviors. They offer a statistical model for evolutionary algorithms that describes a functional relationship between algorithm parameters and performance. Nannen and Eiben \cite{Nannen2007, Nannen2006} propose a statistical approach called REVAC (previously Calibration and Relevance Estimation) to estimating the relevance of parameters in a genetic algorithm. Coy, Golden et al.\ \cite{Coy2001} use a statistical steepest decent method procedure for locating good parameters for metaheuristics on many different combinatorial problem instances.
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@@ -1,3 +1,6 @@
+% This file was created with JabRef 2.7.2.
+% Encoding: UTF-8
+
@ARTICLE{Aamodt1994,
author = {Aamodt, A. and Plaza, E.},
title = {Case-Based Reasoning: Foundational Issues, Methodological Variations,
@@ -746,7 +749,8 @@ @BOOK{Booch1997
title = {Object-Oriented Analysis and Design with Applications},
publisher = {Addison-Wesley},
year = {1997},
- author = {G. Booch and R. Maksimchuk and M. Engle and B. Young and J. Conallen and K. Houston},
+ author = {G. Booch and R. Maksimchuk and M. Engle and B. Young and J. Conallen
+ and K. Houston},
owner = {jasonb},
timestamp = {2010.12.04}
}
@@ -2349,8 +2353,8 @@ @ARTICLE{Droste2006
@INPROCEEDINGS{Du1997,
author = {Du, B. and Gu, J. and Wang, W. and Tsang, D. H. K.},
title = {Multispace search for minimizing the maximum nodal degree},
- booktitle = {Proceedings Sixth International Conference on Computer Communications and
- Networks},
+ booktitle = {Proceedings Sixth International Conference on Computer Communications
+ and Networks},
year = {1997},
editor = {Gu, J.},
pages = {364--367},
@@ -2524,6 +2528,18 @@ @ARTICLE{Feo1995
timestamp = {2010.02.07}
}
+@ARTICLE{Feo1989,
+ author = {T. A. Feo and M. G. C. Resende},
+ title = {A probabilistic heuristic for a computationally difficult set covering
+ problem},
+ journal = {Operations Research Letters},
+ year = {1989},
+ volume = {8},
+ pages = {67--71},
+ owner = {jasonb},
+ timestamp = {2010.02.07}
+}
+
@ARTICLE{Feo1996,
author = {T. A. Feo and K. Sarathy and J. McGahan},
title = {A grasp for single machine scheduling with sequence dependent setup
@@ -2565,18 +2581,6 @@ @ARTICLE{Feo1991
$2$-exchange and insertion exchange.}
}
-@ARTICLE{Feo1989,
- author = {T. A. Feo and M. G. C. Resende},
- title = {A probabilistic heuristic for a computationally difficult set covering
- problem},
- journal = {Operations Research Letters},
- year = {1989},
- volume = {8},
- pages = {67--71},
- owner = {jasonb},
- timestamp = {2010.02.07}
-}
-
@INBOOK{Ferreira2005,
chapter = {Gene Expression Programming and the Evolution of computer programs},
pages = {82--103},
@@ -3597,30 +3601,6 @@ @ARTICLE{Hall2009
timestamp = {2010.01.07}
}
-@ARTICLE{Hansen2001a,
- author = {P. Hansen and N. Mladenovi\'c},
- title = {Variable neighborhood search: Principles and applications},
- journal = {European Journal of Operational Research},
- year = {2001},
- volume = {130},
- pages = {449--467},
- number = {3},
- owner = {jasonb},
- timestamp = {2010.02.05}
-}
-
-@ARTICLE{Hansen2001,
- author = {P. Hansen and N. Mladenovi\'c and D. Perez--Britos},
- title = {Variable Neighborhood Decomposition Search},
- journal = {Journal of Heuristics},
- year = {2001},
- volume = {7},
- pages = {1381--1231},
- number = {4},
- owner = {jasonb},
- timestamp = {2010.02.05}
-}
-
@INBOOK{Hansen2003,
chapter = {6: Variable Neighborhood Search},
pages = {145--184},
@@ -3658,6 +3638,30 @@ @INBOOK{Hansen1998
timestamp = {2010.02.05}
}
+@ARTICLE{Hansen2001a,
+ author = {P. Hansen and N. Mladenovi\'c},
+ title = {Variable neighborhood search: Principles and applications},
+ journal = {European Journal of Operational Research},
+ year = {2001},
+ volume = {130},
+ pages = {449--467},
+ number = {3},
+ owner = {jasonb},
+ timestamp = {2010.02.05}
+}
+
+@ARTICLE{Hansen2001,
+ author = {P. Hansen and N. Mladenovi\'c and D. Perez--Britos},
+ title = {Variable Neighborhood Decomposition Search},
+ journal = {Journal of Heuristics},
+ year = {2001},
+ volume = {7},
+ pages = {1381--1231},
+ number = {4},
+ owner = {jasonb},
+ timestamp = {2010.02.05}
+}
+
@INPROCEEDINGS{Harik1995,
author = {G. Harik},
title = {Finding Multimodal Solutions Using Restricted Tournament Selection},
@@ -4463,7 +4467,7 @@ @INPROCEEDINGS{Kennedy1999
@INPROCEEDINGS{Kennedy1995,
author = {Kennedy, J. and Eberhart, R. C.},
title = {Particle swarm optimization},
- booktitle = {Proceedings IEEE int'l conf. on neural networks Vol. IV},
+ booktitle = {Proceedings of the IEEE International Conference on Neural Networks},
year = {1995},
pages = {1942--1948},
owner = {jasonb},
@@ -4711,7 +4715,8 @@ @TECHREPORT{Kohonen1996a
}
@TECHREPORT{Kohonen1996,
- author = {T. Kohonen and J. Hynninen and J. Kangas and J. Laaksonen and K. Torkkola},
+ author = {T. Kohonen and J. Hynninen and J. Kangas and J. Laaksonen and K.
+ Torkkola},
title = {{LVQ}--{PAK}: The Learning Vector Quantization Program Package},
institution = {Helsinki University of Technology, Laboratory of Computer and Information
Science, Rakentajanaukio},
@@ -5059,8 +5064,8 @@ @ARTICLE{Lin1973
@INPROCEEDINGS{Liu2000,
author = {Liu, P. and Wang, D. W.},
title = {Reduction optimization in heterogeneous cluster environments},
- booktitle = {Proceedings 14th International Parallel and Distributed Processing Symposium
- IPDPS 2000},
+ booktitle = {Proceedings 14th International Parallel and Distributed Processing
+ Symposium IPDPS 2000},
year = {2000},
editor = {Wang, D-W.},
pages = {477--482},
@@ -5194,18 +5199,6 @@ @ARTICLE{Muehlenbein1999
timestamp = {2010.11.25}
}
-@ARTICLE{Muller2002,
- author = {S. D. M\"uller and J. Marchetto and S. Airaghi and P. Koumoutsakos},
- title = {Optimization Based on Bacterial Chemotaxis},
- journal = {IEEE Transactions on Evolutionary Computation},
- year = {2002},
- volume = {6},
- pages = {16--29},
- number = {1},
- owner = {jasonb},
- timestamp = {2010.11.20}
-}
-
@ARTICLE{Muhlenbein1999,
author = {M\"uhlenbein, H. and Mahnig, T. and Rodriguez, A. O.},
title = {Schemata, Distributions and Graphical Models in Evolutionary Optimization},
@@ -5218,6 +5211,18 @@ @ARTICLE{Muhlenbein1999
timestamp = {2010.11.25}
}
+@ARTICLE{Muller2002,
+ author = {S. D. M\"uller and J. Marchetto and S. Airaghi and P. Koumoutsakos},
+ title = {Optimization Based on Bacterial Chemotaxis},
+ journal = {IEEE Transactions on Evolutionary Computation},
+ year = {2002},
+ volume = {6},
+ pages = {16--29},
+ number = {1},
+ owner = {jasonb},
+ timestamp = {2010.11.20}
+}
+
@INPROCEEDINGS{MacNish2005,
author = {C. MacNish},
title = {Benchmarking Evolutionary Algorithms: The {H}uygens Suite},
@@ -5738,6 +5743,17 @@ @ARTICLE{Opitz1999
timestamp = {2008.02.23}
}
+@ARTICLE{Paun2005,
+ author = {G. Pa\v{u}n},
+ title = {Bio-inspired computing paradigms (natural computing)},
+ journal = {Unconventional Programming Paradigms},
+ year = {2005},
+ volume = {3566},
+ pages = {155--160},
+ owner = {jasonb},
+ timestamp = {2010.01.14}
+}
+
@BOOK{Papadimitriou1998,
title = {Combinatorial Optimization: Algorithms and Complexity},
publisher = {Courier Dover Publications},
@@ -5846,17 +5862,6 @@ @BOOK{Patton2005
timestamp = {2010.12.06}
}
-@ARTICLE{Paun2005,
- author = {G. Pa\v{u}n},
- title = {Bio-inspired computing paradigms (natural computing)},
- journal = {Unconventional Programming Paradigms},
- year = {2005},
- volume = {3566},
- pages = {155--160},
- owner = {jasonb},
- timestamp = {2010.01.14}
-}
-
@BOOK{Peck2005,
title = {Introduction to Statistics and Data Analysis},
publisher = {Duxbury Publishing},
@@ -7425,7 +7430,8 @@ @INPROCEEDINGS{Voudouris1998
@INPROCEEDINGS{Wang1991,
author = {Wang, C. J. and Tsang, E. P. K. },
title = {Solving constraint satisfaction problems using neural networks},
- booktitle = {Proceedings Second International Conference on Artificial Neural Networks},
+ booktitle = {Proceedings Second International Conference on Artificial Neural
+ Networks},
year = {1991},
pages = {295--299},
owner = {jasonb},
@@ -8000,3 +8006,4 @@ @BOOK{Pelikan2006
owner = {jasonb},
timestamp = {2010.11.25}
}
+

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