Foundations of Generic Optimization: Volume 1: A Combinatorial Approach to EpistasisSpringer Science & Business Media, 6 jul 2005 - 298 pagina's Annotation The success of a genetic algorithm when applied to an optimization problem depends upon several features present or absent in the problem to be solved, including the quality of the encoding of data, the geometric structure of the search space, deception or epistasis. This book deals essentially with the latter notion, presenting, for the first time, a complete state-of-the-art of research on this notion, in a structured, completely self-contained and methodical way. In particular, it contains a refresher on the linear algebra used in the text as well as an elementary introductory chapter on genetic algorithms aimed at readers unacquainted with this notion. In this way, the monograph aims to serve a broad audience consisting of graduate and advanced undergraduate students in mathematics and computer science, as well as researchers working in the domains of optimization, artificial intelligence, theoretical computer science, combinatorics and evolutionary algorithms. |
Inhoudsopgave
I | 3 |
III | 17 |
V | 21 |
VI | 25 |
VII | 27 |
VIII | 29 |
IX | 30 |
X | 32 |
LV | 141 |
LVI | 147 |
LVII | 151 |
LIX | 153 |
LX | 162 |
LXI | 164 |
LXII | 165 |
LXIII | 168 |
XI | 34 |
XII | 40 |
XIII | 42 |
XV | 43 |
XVI | 45 |
XVII | 47 |
XX | 48 |
XXI | 50 |
XXII | 51 |
XXV | 56 |
XXVI | 59 |
XXVII | 61 |
XXIX | 67 |
XXX | 73 |
XXXI | 74 |
XXXIII | 83 |
XXXIV | 88 |
XXXV | 89 |
XXXVII | 90 |
XXXVIII | 91 |
XXXIX | 92 |
XL | 96 |
XLI | 99 |
XLIII | 106 |
XLIV | 112 |
XLV | 115 |
XLVII | 116 |
XLVIII | 117 |
XLIX | 120 |
L | 123 |
LI | 128 |
LII | 132 |
LIII | 137 |
Overige edities - Alles bekijken
Foundations of Generic Optimization: Volume 1: A Combinatorial Approach to ... M. Iglesias,B. Naudts,A. Verschoren,C. Vidal Geen voorbeeld beschikbaar - 2005 |
Veelvoorkomende woorden en zinsdelen
applied argue by induction assume average fitness balanced sum theorem building blocks calculate chapter characteristic polynomial columns components compute consider Corollary correlation corresponding crossover defined denote dimension dynamics eigenvalues eigenvectors encoding epistasis value epistasis variance example fitness distribution fitness function fitness value function f genetic algorithm given H₁ h₂ Hamming distance individual interactions ISBN Lemma linear map linearly independent local optimum maximal Minimal epistasis non-zero normalized epistasis notations Note NP-complete obtain Ol-n optimization optimum order functions orthogonal partition coefficients population positive integer previous result probability problem difficulty Proof properties Proposition recursion representation Royal Road functions scalar schema H schema theorem schemata search problem search space selection solution strings of length subset subspace template functions V₁ vector space Walsh coefficients Walsh functions Walsh matrix Walsh transform Wkni yields zero ΕΣ Σ Σ ΣΣ
Populaire passages
Pagina 292 - L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, 1991.
Pagina 288 - A square matrix is said to be diagonalizable if it is similar to a diagonal matrix. It can be proved that a square matrix A is diagonalizable if and only if there is a basis for R" consisting of eigenvectors of A.