Evolutionary Computation: Toward a New Philosophy of Machine IntelligenceIEEE Press, 1995 - Всего страниц: 272 The Third Edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does. |
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Стр. 102
... distribution . For some specific applications , the coding strings contain only genetic information ( e.g. , a computer program ) that must operate in a specific environment in order to generate any observed be- havior.2 Mutations are ...
... distribution . For some specific applications , the coding strings contain only genetic information ( e.g. , a computer program ) that must operate in a specific environment in order to generate any observed be- havior.2 Mutations are ...
Стр. 134
... distribution has mean μ , and variance of while the second distribution has mean μ1⁄2 < μ , and variance o2 >> 02. To minimize the expected loss ( Eq . 4-9 ) , all trials should be devoted to the first distribution because it has the ...
... distribution has mean μ , and variance of while the second distribution has mean μ1⁄2 < μ , and variance o2 >> 02. To minimize the expected loss ( Eq . 4-9 ) , all trials should be devoted to the first distribution because it has the ...
Стр. 135
... Distribution # 2 Distribution # 1 με με w = worth Figure 4-3 Minimizing expected losses does not always correspond to maximizing potential gains . The illustration depicts two distributions of the fitness of various strings contained ...
... Distribution # 2 Distribution # 1 με με w = worth Figure 4-3 Minimizing expected losses does not always correspond to maximizing potential gains . The illustration depicts two distributions of the fitness of various strings contained ...
Содержание
NATURAL EVOLUTION | 37 |
COMPUTER SIMULATION | 67 |
37 | 113 |
Авторские права | |
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Другие издания - Просмотреть все
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence David B. Fogel Ограниченный просмотр - 2006 |
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence David B. Fogel Просмотр фрагмента - 2006 |
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence David B. Fogel Недоступно для просмотра - 2006 |
Часто встречающиеся слова и выражения
absorbing adaptive analysis Artificial Intelligence Atmar Bäck best-evolved bit mutation bit strings Bremermann chromosome coding complex components Conf Conrad convergence rate crossover Davis defection defined described distribution dynamic parameter encoding edited environment error evaluated evolution strategies evolutionary algorithms evolutionary computation Evolutionary Programming evolved expert system Figure finite state machines fitness Fogel fuzzy gene genetic algorithm genotype global optimum Gould Holland indicates individual initial input inversion iteration Jong learning Markov chain Mayr mean method Minsky Morgan Kaufmann mutual cooperation natural selection Neural Networks observed offspring operators organisms parents perceptron performance phenotypic play player pleiotropy pole population possible prediction probability problems Proc procedure random variable randomly recombination reproduction response surface samples schemata Schraudolph Schraudolph and Belew Schwefel score sequence solutions specific standard deviation stochastic symbols theoretical tion transition matrix trials two-point uniform crossover vector