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With the development of information technology, especially the widespread use of Web, information on Web increases rapidly and becomes a huge information resource. In the meanwhile, such abundant information makes it an urgent problem: how to extract useful content rapidly and efficiently from information resources. This paper proposes a framework for evolutionary systems to search implicit knowledge on the web. Based on Cultural Algorithms (CA), the web search process is supported by the domain knowledge objects in the belief space, and the optimization process is supported by evolutionary search in the population space. This framework in web search may help increase competition and diversity on the web.