Title: Knowledge constrained evolutionary algorithms: a case study for financial investing

Authors: Jie Du; Hayden Wimmer; Roy Rada

Addresses: School of Computing and Information Systems, Grand Valley State University, 1 Campus Drive, Allendale, MI 49401-9403, USA ' Department of Business Education, Information and Technology Management, Bloomsburg University of Pennsylvania, 400 E Second St., Bloomsburg, PA 17815-1301, USA ' Department of Information Systems, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA

Abstract: The purpose of this paper is to examine the role of domain knowledge in guiding evolution. The hypothesis examined in this paper is that the use of knowledge represented as a semantic network will bias mutation so that changes in structure measured by the semantic net correspond to changes in function. This hypothesis is tested utilising information and data from the finance domain. In this paper relevant literature is reviewed and an experimental framework is proposed which incorporates knowledge in evolution. An empirical investigation is presented to demonstrate the role of knowledge and gradualness in evolution. Future work will involve investigating methods to identify or construct a semantic network which is 'meaningful' to humans as well as machines.

Keywords: knowledge constraints; evolutionary algorithms; gradualness; mutation; financial investment; domain knowledge; semantic networks.

DOI: 10.1504/IJAISC.2014.065801

International Journal of Artificial Intelligence and Soft Computing, 2014 Vol.4 No.4, pp.335 - 353

Accepted: 05 Jul 2014
Published online: 29 Nov 2014 *

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