typo of multi-restart

Jason2Brownlee · Feb 5, 2013 · 83b2e0c · 83b2e0c
1 parent d0afcac
commit 83b2e0c
Show file tree

Hide file tree

Showing 3 changed files with 6 additions and 4 deletions.
diff --git a/book/a_stochastic/iterated_local_search.tex b/book/a_stochastic/iterated_local_search.tex
@@ -24,13 +24,13 @@ \subsection{Taxonomy}
 % To what fields of study does a technique belong?
 Iterated Local Search is a Metaheuristic and a Global Optimization technique.
 % What are the closely related approaches to a technique?
-It is an extension of Mutli-Restart Search and may be considered a parent of many two-phase search approaches such as the Greedy Randomized Adaptive Search Procedure (Section~\ref{sec:grasp}) and Variable Neighborhood Search (Section~\ref{sec:variable_neighborhood_search}).
+It is an extension of Multi-Restart Search and may be considered a parent of many two-phase search approaches such as the Greedy Randomized Adaptive Search Procedure (Section~\ref{sec:grasp}) and Variable Neighborhood Search (Section~\ref{sec:variable_neighborhood_search}).
 
 % Strategy: Problem solving plan
 % The strategy is an abstract description of the computational model. The strategy describes the information processing actions a technique shall take in order to achieve an objective. The strategy provides a logical separation between a computational realization (procedure) and a analogous system (metaphor). A given problem solving strategy may be realized as one of a number specific algorithms or problem solving systems. The strategy description is textual using information processing and algorithmic terminology.
 \subsection{Strategy}
 % What is the information processing objective of a technique?
-The objective of Iterated Local Search is to improve upon stochastic Mutli-Restart Search by sampling in the broader neighborhood of candidate solutions and using a Local Search technique to refine solutions to their local optima.
+The objective of Iterated Local Search is to improve upon stochastic Multi-Restart Search by sampling in the broader neighborhood of candidate solutions and using a Local Search technique to refine solutions to their local optima.
 % What is a techniques plan of action?
 Iterated Local Search explores a sequence of solutions created as perturbations of the current best solution, the result of which is refined using an embedded heuristic.
 

diff --git a/book/b_errata.tex b/book/b_errata.tex
@@ -17,6 +17,7 @@ \section*{Revision 1}
 	\item[page 32] Typo in References section of Random Search.
   \item[page 37] Fixed bug with \texttt{step\_size} in Adaptive Random Search implementation. Thanks to Zach Scott.
 	\item[page 43] Typo in Taxonomy section of Iterated Local Search. Thanks to Diego Noble.
+	\item[page 43--47] Typo's of Multi-Restart. Thanks to Stephan Williams.
 	\item[page 69] Bug in \texttt{recombine} function of the Scatter Search algorithm. Thanks to Markus Stokmaier.
 	\item[page 111] Bug in the \texttt{init\_population} function of the Evolution Strategies algorithm. Thanks to Lai Yu-Hsuan.
 	\item[page 129] Bug in the \texttt{one\_point\_crossover} function of the Grammatical Evolution implementation. Thanks to Mark Chenoweth.

diff --git a/book/f_acknowledgments.tex b/book/f_acknowledgments.tex
@@ -33,8 +33,9 @@ \chapter*{Acknowledgments\markboth{Acknowledgments}{}}
 Mark Chenoweth,
 Markus Stokmaier,
 Stefan Pauleweit,
-and
-Lai Yu-Hsuan.
+Lai Yu-Hsuan,
+and 
+Stephan Williams.
 
 % cover voting
 Thanks to the hundreds of machine learning enthusiasts who voted on potential covers and helped shape what this book became. You know who you are!