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Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR taskdiscovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is well known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs on GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations on the design of fitness functions for genetic-based information retrieval experiments.

Automatlc feedback methods may be used In onllne Infor. matlon retrleval to generate Improved query statements based on Information contained In prevlously retrieved documents. In thls study automatlc relevance feedback techniques are applled to Boolean query statements. The feedback operatlons are carrled out uslng both the conventional Boolean loglc, as well as an extended loglc pre duclng Improved retrleval effectlvenees. Experlmental output Is Included to evaluate the automatic feedback operations. JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE. 36(3):200-210; 1985 CCC0002-8231/85/030200-11$04.00 I @ 12 @ @ 14 15 0 16 17 18 @ 20 I 21 @ 23 24 25 26 27 28 29 30 ... 1 Use items @, @, @, a, @ to construct second iteration feedback query Sample initial ranking continued twice (freeze no 4nl relevant in top 10 + no)I @ 21 @ @ @ 23 0 24 25 26 @ 27 28 29 30 I 31 ...Use items 0, @, 0, @, 0, @ to construct third iteration feedback query FIG. 4. Example of partial rank freezing (the numbers represent individual documents listed in retrieval order).

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