Reference Hub1
Evaluation of Genetic Algorithm as Learning System in Rigid Space Interpretation

Evaluation of Genetic Algorithm as Learning System in Rigid Space Interpretation

Bhupesh Kumar Singh
ISBN13: 9781466644502|ISBN10: 1466644508|EISBN13: 9781466644519
DOI: 10.4018/978-1-4666-4450-2.ch016
Cite Chapter Cite Chapter

MLA

Singh, Bhupesh Kumar. "Evaluation of Genetic Algorithm as Learning System in Rigid Space Interpretation." Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, edited by Pandian M. Vasant, IGI Global, 2014, pp. 475-510. https://doi.org/10.4018/978-1-4666-4450-2.ch016

APA

Singh, B. K. (2014). Evaluation of Genetic Algorithm as Learning System in Rigid Space Interpretation. In P. Vasant (Ed.), Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications (pp. 475-510). IGI Global. https://doi.org/10.4018/978-1-4666-4450-2.ch016

Chicago

Singh, Bhupesh Kumar. "Evaluation of Genetic Algorithm as Learning System in Rigid Space Interpretation." In Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, edited by Pandian M. Vasant, 475-510. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4450-2.ch016

Export Reference

Mendeley
Favorite

Abstract

Genetic Algorithm (GA) (a structured framework of metaheauristics) has been used in various tasks such as search optimization and machine learning. Theoretically, there should be sound framework for genetic algorithms which can interpret/explain the various facts associated with it. There are various theories of the working of GA though all are subject to criticism. Hence an approach is being adopted that the legitimate theory of GA must be able to explain the learning process (a special case of the successive approximation) of GA. The analytical method of approximating some known function is expanding a complicated function an infinite series of terms containing some simpler (or otherwise useful) function. These infinite approximations facilitate the error to be made arbitrarily small by taking a progressive greater number of terms into consideration. The process of learning in an unknown environment, the form of function to be learned is known only by its form over the observation space. The problem of learning the possible form of the function is termed as experience problem. Various learning paradigms have ensured their legitimacy through the rigid space interpretation of the concentration of measure and Dvoretzky theorem. Hence it is being proposed that the same criterion should be applied to explain the learning capability of GA, various formalisms of explaining the working of GA should be evaluated by applying the criteria, and that learning capability can be used to demonstrate the probable capability of GA to perform beyond the limit cast by the No Free Lunch Theorem.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.