{"title":"复杂性理论与遗传学","authors":"P. Pudlák","doi":"10.1109/SCT.1994.315787","DOIUrl":null,"url":null,"abstract":"We introduce a population genetics model in which the operators are effectively computable-computable in polynomial time on probabilistic Turing machines. We shall show that in this model a population can encode easily large amount of information from environment into genetic code. Then it can process the information as a parallel computer. More precisely, we show that it can stimulate polynomial space computations in polynomially many steps, even if the recombination rules are very simple.<<ETX>>","PeriodicalId":386782,"journal":{"name":"Proceedings of IEEE 9th Annual Conference on Structure in Complexity Theory","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Complexity theory and genetics\",\"authors\":\"P. Pudlák\",\"doi\":\"10.1109/SCT.1994.315787\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce a population genetics model in which the operators are effectively computable-computable in polynomial time on probabilistic Turing machines. We shall show that in this model a population can encode easily large amount of information from environment into genetic code. Then it can process the information as a parallel computer. More precisely, we show that it can stimulate polynomial space computations in polynomially many steps, even if the recombination rules are very simple.<<ETX>>\",\"PeriodicalId\":386782,\"journal\":{\"name\":\"Proceedings of IEEE 9th Annual Conference on Structure in Complexity Theory\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of IEEE 9th Annual Conference on Structure in Complexity Theory\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCT.1994.315787\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of IEEE 9th Annual Conference on Structure in Complexity Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCT.1994.315787","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We introduce a population genetics model in which the operators are effectively computable-computable in polynomial time on probabilistic Turing machines. We shall show that in this model a population can encode easily large amount of information from environment into genetic code. Then it can process the information as a parallel computer. More precisely, we show that it can stimulate polynomial space computations in polynomially many steps, even if the recombination rules are very simple.<>
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