optinformatics for schema analysis of binary genetic algorithms
Given the importance of optimization and informatics which are the two broad fields of research, we present an instance of Optinformatics which denotes the specialization of informatics for the processing of data generated in optimization so as to extract possibly implicit and potentially useful information and knowledge . In particular, evolutionary computation does not have to be entirely a black-box approach that generates only the global optimal or good quality solutions. How the solutions are obtained in evolutionary search may be brought to light through Optinformatics. In this paper, we present a Frequent Schemas Analysis (FSA) technique for extracting knowledge from the search process by using the historical optimization data, which are otherwise often discarded. FSA bring about greater understanding of GA dynamics through mining for frequent schemas that exists implicitly within the optimization data via the design of frequent pattern techniques (LoFIA) in informatics. To illustrate the principle of optinformatics, a case study using the Royal Road problem is used to explain the search performance of Genetic Algorithm (GA) in action.
genetic algorithms royal road problem schema theory frequent pattern mining