Introduction to Genetic AlgorithmsTheoriginofevolutionaryalgorithmswasanattempttomimicsomeoftheprocesses taking place in natural evolution. Although the details of biological evolution are not completely understood (even nowadays), there exist some points supported by strong experimental evidence: • Evolution is a process operating over chromosomes rather than over organisms. The former are organic tools encoding the structure of a living being, i.e., a cr- ture is “built” decoding a set of chromosomes. • Natural selection is the mechanism that relates chromosomes with the ef ciency of the entity they represent, thus allowing that ef cient organism which is we- adapted to the environment to reproduce more often than those which are not. • The evolutionary process takes place during the reproduction stage. There exists a large number of reproductive mechanisms in Nature. Most common ones are mutation (that causes the chromosomes of offspring to be different to those of the parents) and recombination (that combines the chromosomes of the parents to produce the offspring). Based upon the features above, the three mentioned models of evolutionary c- puting were independently (and almost simultaneously) developed. |
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Contents
| 1 | |
Genetic Algorithms | 15 |
Terminologies and Operators of GA | 39 |
Exercise Problems | 81 |
Exercise Problems | 103 |
Exercise Problems | 129 |
Exercise Problems | 163 |
Genetic Algorithm Optimization Problems | 165 |
Exercise Problems | 209 |
Genetic Algorithm Optimization in CC++ | 263 |
Applications of Genetic Algorithms | 317 |
Introduction to Particle Swarm Optimization and Ant Colony | 403 |
Bibliography | 425 |
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Common terms and phrases
adaptive allows ants applied approach assigned attributes better binary blocks building called cell chromosome combination combinatorial optimization components considered constraints contain convergence cost create crossover defined determine developed dominance edge encoding evaluation evolution evolutionary example fitness function fitness value follows fuzzy gene genetic algorithm genetic programming given implemented important improve increase individuals initial input iteration knowledge learning length machine mating method minimize mutation natural nodes objective function obtained offspring operators optimization problems output parallel parameters parents performance population position possible probability produce random randomly reliability representation represents requires Research roulette wheel selection rules scheduling scheme search space selection shown shows similar simple single solution solve specific Step string structure techniques terminal tion tree variables various
Bibliographic information
| Title | Introduction to Genetic Algorithms |
| Authors | S.N. Sivanandam, S. N. Deepa |
| Edition | illustrated |
| Publisher | Springer Science & Business Media, 2007 |
| ISBN | 3540731903, 9783540731900 |
| Length | 442 pages |
| Subjects | › › Computers / Artificial Intelligence / General Mathematics / Applied Technology & Engineering / Engineering (General) Technology & Engineering / General |
| Export Citation | BiBTeX EndNote RefMan |