Evolutionary Intelligence: An Introduction to Theory and Applications with MatlabSpringer Science & Business Media, 15 мая 2008 г. - Всего страниц: 584 This book provides a highly accessible introduction to evolutionary computation. It details basic concepts, highlights several applications of evolutionary computation, and includes solved problems using MATLAB software and C/C++. This book also outlines some ideas on when genetic algorithms and genetic programming should be used. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this. |
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Стр. xv
... Grammar Development . 266 6.3.3 Grammar Evolution . 267 6.3.4 GRAEL - 1 : Probabilistic Grammar Optimization 268 6.3.5 GRAEL - 2 : Grammar Rule Discovery 272 7 Introduction . 6.3.6 GRAEL - 3 : Unsupervised Grammar Contents XV.
... Grammar Development . 266 6.3.3 Grammar Evolution . 267 6.3.4 GRAEL - 1 : Probabilistic Grammar Optimization 268 6.3.5 GRAEL - 2 : Grammar Rule Discovery 272 7 Introduction . 6.3.6 GRAEL - 3 : Unsupervised Grammar Contents XV.
Стр. xvii
... Rules .368 Results .... 8.1.2 Evolving Robocode Strategies using Genetic Programming . 369 8.1.3 374 8.1.4 Concluding Remarks 375 Prediction of Biochemical Reactions using Genetic Programming .. 375 8.2.1 Contents xvii.
... Rules .368 Results .... 8.1.2 Evolving Robocode Strategies using Genetic Programming . 369 8.1.3 374 8.1.4 Concluding Remarks 375 Prediction of Biochemical Reactions using Genetic Programming .. 375 8.2.1 Contents xvii.
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... Rules 8.8.4 Concluding Remarks ... 8.9 Artificial Neural Network Development by Means of Genetic Programming with Graph Codification 8.9.1 State of the Art 8.9.2 Model ... 9 9.1 Timetabling Problem ... 9.1.1 Introduction .. 8.9.3 ...
... Rules 8.8.4 Concluding Remarks ... 8.9 Artificial Neural Network Development by Means of Genetic Programming with Graph Codification 8.9.1 State of the Art 8.9.2 Model ... 9 9.1 Timetabling Problem ... 9.1.1 Introduction .. 8.9.3 ...
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... Rules . . . . . 529 C.20 An Evolutionary Algorithm for Solving Nonlinear Bilevel Programming Based on a New Constraint - handling Scheme C.21 Evolutionary Fuzzy Neural Networks for Hybrid Financial Prediction . 530 530 C.22 Genetic ...
... Rules . . . . . 529 C.20 An Evolutionary Algorithm for Solving Nonlinear Bilevel Programming Based on a New Constraint - handling Scheme C.21 Evolutionary Fuzzy Neural Networks for Hybrid Financial Prediction . 530 530 C.22 Genetic ...
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Содержание
Introduction to Evolutionary Computation | 1 |
Summary | 30 |
Principles of Evolutionary Algorithms | 31 |
Genetic Algorithms with Matlab | 77 |
NonConvex Function | 132 |
Genetic Programming Concepts | 171 |
Parallel Genetic Algorithms | 219 |
Applications of Evolutionary Algorithms | 249 |
Genetic Programming Applications | 367 |
Applications of Parallel Genetic Algorithm | 445 |
Appendix A Glossary | 503 |
Appendix B Abbreviations | 517 |
Programming Based on a New Constrainthandling Scheme | 530 |
Appendix D MATLAB Toolboxes | 533 |
Appendix F Ga Source Codes in C Language | 547 |
Appendix G EC ClassCode Libraries and Software Kits | 559 |
with Evolutionary Algorithms | 282 |
Applications of Genetic Algorithms | 297 |
Bibliography | 569 |
Другие издания - Просмотреть все
Evolutionary Intelligence: An Introduction to Theory and Applications with ... S. Sumathi,T. Hamsapriya,P. Surekha Недоступно для просмотра - 2009 |
Evolutionary Intelligence: An Introduction to Theory and Applications with ... S. Sumathi,T. Hamsapriya,P. Surekha Недоступно для просмотра - 2008 |
Часто встречающиеся слова и выражения
adaptive annealing application approach average best individual binary chosen chromosome complex components constraints convergence created crossover crossover operator data types defined demes distributed domain encoding evaluation evolution evolution strategies evolutionary algorithm Evolutionary Computation Evolutionary Programming evolved example fingerprint fitness function fitness value Fuzzy genes genetic algorithm genetic operators genetic programming genotype global optimization grammar graph implementation initial population input integer iteration length MATLAB maximum method migration mutation operator mutation rate neural network neuron node objective function offspring optimal solution optimization problems output parameters parents parse tree performance plot possible probability processors produce random randomly recombination representation represented reproduction rules scheduling schema search space segmentation sequence shown in Figure simulated simulated annealing solve step strategies string structure subpopulations Table takeImage target techniques terminal tion topology tournament selection variables vector waveform