Growing interest in traditional knowledge (TK), particularly in relation to the prediction of weather or climate extremes, raises issues concerning the appropriate storage and management of the information collected. The Traditional Knowledge Database (TK Database) for the storage and use of TK associated with weather and climate prediction in the Pacific was designed with the following principles in mind: (1) preservation of the knowledge, maintaining cultural context wherever possible; (2) respect for intellectual property and cultural sensitivities around data sharing and use; (3) appropriate system design, accounting for ongoing costs of system maintenance and often intermittent Internet access; and (4) moving beyond data preservation to ensure continued use and growth of the TK. The TK Database was successfully deployed to four countries in the south Pacific and is regularly used by their national meteorological services, and partner organizations, both to preserve TK related to weather and climate and as a tool to assist in monitoring the TK indicators. As the first database of its kind, the TK Database fills a critical gap in the appropriate storage and application of TK and provides an important foundation for future developments.

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Genetic Algorithms have been used to solve difficult optimization problems in a number of fields. However, in order to solve a problem with GA, the user has to specify a number of parameters. This parameter tuning is a difficult task as different genetic operators are suitable in different application areas. This paper proposes a scheme for genetic algorithms where the genetic operators are changed randomly. The information of gender and age is also incorporated in this approach to maintain population diversity. The experimental result of the proposed algorithm based on a mechanical design problem shows promising result.