Biblio

Computational Intelligence (CI) algorithms/techniques are packaged in a variety of disparate frameworks/applications that all vary with respect to specific supported functionality and implementation decisions that drastically change performance. Developers looking to employ different CI techniques are faced with a series of trade-offs in selecting the appropriate library/framework. These include resource consumption, features, portability, interface complexity, ease of parallelization, etc. Considerations such as language compatibility and familiarity with a particular library make the choice of libraries even more difficult. The paper introduces MeetCI, an open source software framework for computational intelligence software design automation that facilitates the application design decisions and their software implementation process. MeetCI abstracts away specific framework details of CI techniques designed within a variety of libraries. This allows CI users to benefit from a variety of current frameworks without investigating the nuances of each library/framework. Using an XML file, developed in accordance with the specifications, the user can design a CI application generically, and utilize various CI software without having to redesign their entire technology stack. Switching between libraries in MeetCI is trivial and accessing the right library to satisfy a user's goals can be done easily and effectively. The paper discusses the framework's use in design of various applications. The design process is illustrated with four different examples from expert systems and machine learning domains, including the development of an expert system for security evaluation, two classification problems and a prediction problem with recurrent neural networks.