{'fraction_non_alphanumeric': 0.011757021554539516, 'fraction_numerical': 0.0, 'mean_word_length': 5.079365079365079, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Combining Relevance Feedback and Genetic Algorithm in an Internet Information Filtering Engine

Ever since the advent of the public network Internet, the quantity of available information is rapidly rising. One of the most important use of this public network is to find information. In such a huge and unstable information collection, today's greatest problem is to find relevant information. 
 
This paper presents the development of IntellAgent, an Internet information filtering agent with a search engine using a hybrid evolutionary algorithm to optimize the user profile. The algorithm is a combination of the well known relevance feedback process and a genetic algorithm. This paper describes in detail the specifics of the combination and reports on its effectiveness measured using the TREC collection. 
 
The agent builds its own data collection statistics incrementally as it analyses the documents of the collection. Two construction methods are tested: one using all terms of all documents and a second one using only the the profile terms. The hybrid algorithm is tested against both methods. 
 
The results show that the hybrid algorithm performs better in recall than the relevance feedback process alone and better in precision than the genetic algorithm alone, revealing that the combination is building upon the strength of both processes. Using fire incremental statistics collection built with the profile matching terms shows the same level of effectiveness but a slightly different ratio of recall/precision.