## Estimation of Distribution Algorithms: A New Tool for Evolutionary ComputationEstimation 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 |

375 | |

379 | |

### Common terms and phrases

abductive inference accuracy applied arc modifications Artificial Intelligence Baluja Basque Country Bayesian networks binary CGNs chapter classifier combinatorial optimization Computer Science Conference continuous domains continuous EDAs convergence crossover data clustering database datasets denotes density function Department of Computer dimension discrete EDAs Distribution Algorithms EBNA EDA approaches editors EGNAee EMDA encoding Estimation of Distribution Etxeberria eval evaluation function Evolution Strategies evolutionary algorithms Evolutionary Computation experimental results feature subset Figure fitness value FSS-EBNA FW-EBNA Gaussian networks Genetic Algorithms global optima Goldberg heuristic IEEE iteration joint probability distribution knapsack problem Larranaga Lozano Machine Learning method Miihlenbein MIMIC Morgan Kaufmann mutation nodes number of evaluations operators optimization problems optimum PA1BIC parameters partition PBIL Pelikan performance permutation probabilistic graphical models probabilistic models proposed Pseudocode recombination representation rithm sampling Schwefel search space Section selected individuals solution Solving Step stopping criterion Table Technical Report tion UMDA UMDAC vector

### Popular passages

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