Incorporating domain-specific knowledge into a genetic algorithm to implement case-based reasoning adaptation

摘要

In case-based reasoning systems the adaptation phase is a notoriously difficult and complex step. The design and implementation of an effective case adaptation algorithm is generally determined by the type of application which decides the nature and the structure of the knowledge to be implemented within the adaptation module, and the level of user involvement during this phase. A new adaptation approach is presented in this paper which uses a modified genetic algorithm incorporating specific domain knowledge and information provided by the retrieved cases. The approach has been developed for a CBR system (CBEM) supporting the use and design of numerical models for estuaries. The adaptation module finds the values of hundreds of parameters for a selected numerical model retrieved from the case-base that is to be used in a new problem context. Without the need of implementing very specific adaptation rules, the proposed approach resolves the problem of acquiring adaptation knowledge by combining the search power of a genetic algorithm with the guidance provided by domain-specific knowledge. The genetic algorithm consists of a modifying version of the classical genetic operations of initialisation, selection, crossover and mutation designed to incorporate practical but general principles of model calibration without reference to any specific problems. The genetic algorithm focuses the search within the parameters' space on those zones that most likely contain the required solutions thus reducing computational time. In addition, the design of the genetic algorithm-based adaptation routine ensures that the parameter values found are suitable for the model approximation and hypotheses, and complies with the problem domain features providing correct and realistic model outputs. This adaptation method is suitable for case-based reasoning systems dealing with numerical modelling applications that require the substitution of a large number of parameter values.