Selection is one of the key operations of genetic algorithm (GA). This paper presents a comparative analysis of GA performance in solving multi-objective network design problem (MONDP) using different parent selection methods. Three problem instances were tested and results show that on the average tournament selection is the most effective and most efficient for 10-node network design problem, while Ranking & Scaling is the least effective and least efficient. For 21-node and 36-node network problems, Roulette Wheel is the least effective but most efficient while Ranking & Scaling equals and outperformed tournament in effectiveness and efficiency respectively.

This paper continues the advancement of models proposed for Information Retrieval by understanding that, the Information Retrieval task continues to draw attention as the information repositories increase. Knowing that Natural Language presentation of user's information need help to reduce the complexity of the search process, we propose the use of a well defined Significant Indicator, which uses the relevance index of terms derived from the position of the text, to perform retrieval. This is achieved by initiating a text wrapping process such that document representation in space could algebraically be measured and assigned appropriate function as similarity ratio for Query and Document. Benchmark tools for Information Retrieval were followed and experiment performed using TREC classified data implemented with TRECEVAL shows better performance against some baseline models. The paper suggests further research in the direction of the Significant Indicator as a method for large search space reduction.

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