Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

Front Cover
Pedro Larrañaga, Jose A Lozano
Springer Science & Business Media, 2002 - Computers - 382 pages
0 Reviews
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited.
This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks.
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science.
`... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana.
  

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

VII
3
VIII
6
IX
14
X
19
XI
20
XII
27
XIII
28
XIV
31
LXI
226
LXII
231
LXIII
233
LXIV
234
LXV
237
LXVI
240
LXVII
243
LXVIII
244

XV
44
XVI
51
XVIII
57
XIX
58
XX
64
XXI
80
XXII
90
XXIII
101
XXIV
103
XXV
105
XXVI
111
XXVII
113
XXVIII
123
XXIX
129
XXX
130
XXXI
133
XXXII
138
XXXIII
142
XXXIV
147
XXXV
148
XXXVI
155
XXXVII
159
XXXVIII
161
XXXIX
165
XL
167
XLI
168
XLII
169
XLIII
173
XLIV
177
XLV
181
XLVI
182
XLVII
183
XLVIII
185
XLIX
193
L
195
LI
196
LII
197
LIII
202
LIV
203
LVI
208
LVII
211
LVIII
212
LIX
217
LX
221
LXIX
245
LXX
247
LXXI
254
LXXII
256
LXXIII
257
LXXIV
262
LXXV
267
LXXVI
269
LXXVII
271
LXXVIII
273
LXXIX
282
LXXX
289
LXXXI
295
LXXXII
296
LXXXIII
299
LXXXIV
302
LXXXV
308
LXXXVI
313
LXXXVII
314
LXXXVIII
315
LXXXIX
318
XC
320
XCI
323
XCII
324
XCIV
326
XCV
327
XCVI
330
XCVII
331
XCVIII
338
XCIX
343
C
345
CII
347
CIII
351
CIV
352
CV
355
CVI
361
CVII
362
CVIII
363
CX
368
CXI
373
CXII
375
CXIII
379
Copyright

Common terms and phrases

Popular passages

Page 377 - Hanson, T. (1989). Optimizing neural networks using faster, more accurate genetic search.

References to this book

All Book Search results »

Bibliographic information