Crossover for single-objective numerical optimization problems
Tomasz Gwiazda, Apr 12, 2006 - 408 pages
This book is the first of the series of reference books I am working on, with the aim to provide a possibly most comprehensive review of methods developed in the field of Genetic Algorithms. The necessity to concentrate on certain thematic areas is the result of the character of these books. The choice of those areas, even though performed arbitrarily will hopefully reflect their degree of importance and popularity. Hence, in this book which begins the whole series, an operator of the greatest importance for Genetic Algorithms will be presented i.e. crossover operator and its area of application will be single objective numerical optimization problems. This edition contains descriptions of 11 standard, 66 binary coded, and 89 real coded crossover operators; 182 algorithms in a form of pseudo code; and 453 active URLs pointing to sites with referenced papers. My Internet page (www.tomaszgwiazda.pl) offers the first 40 pages of this book. You can also find a review written for Polish edition of my work.
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1-Point Crossover 1(titiac Adaptive Probability applied Arithmetical Crossover binary bits choose a uniform Conference on Evolutionary Congress on Evolutionary Constraint Satisfaction Problems convergence speed create offspring create one offspring create two offspring Crossover Algorithm crossover Motivation crossover operator crossover points Crossover-2 Eiben A.E. end do ___________________________________________________________________ end do Comments Evolutionary Algorithms Evolutionary Computation Evolutionary Computation Conference Experiment domains fitness value follows function Compared genes Genetic Algorithms Genetic and Evolutionary IEEE International Conference IEEE Transactions Jong’s functions method Morgan Kaufman Mühlenbein H Multiple Crossover n/a ___________________________________________________________________ offspring C(t+1 Orthogonal Crossover parameter parent pool parent vectors parents A(t parents rows premature convergence problem Compared Proceedings of IEEE random real number randomly choose randomly select Read Real-Coded Genetic Algorithms select two parents solution space solution vector Source text substrings Taguchi Taguchi Method transposon Uniform Crossover ___________________________________________________________________ uniform random real α α