TY - JOUR
T1 - Good Versus Bad Knowledge: Ontology Guided Evolutionary Algorithms
AU - Wimmer, Hayden
AU - Rada, Roy
N1 - Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/11/30
Y1 - 2015/11/30
N2 - Abstract Good knowledge would be expected to help a knowledge-based algorithm more than bad knowledge. In this research, the precise effect of good versus bad knowledge on evolutionary algorithms is explored. The testable hypothesis of this paper is that good knowledge will have a significant effect on the evolutionary mutation process, whereas bad knowledge will have no significant effect. A knowledge-guided evolutionary algorithm is developed where ontologies, representing knowledge, are applied to the mutation process. Bad knowledge is represented as a randomly generated ontology, while good knowledge is represented by ontologies constructed with domain knowledge and following a formal ontology development process. Decision trees are evolved to solve a classification problem. Fitness is classification accuracy. The experiment is replicated over 2 data-sets from different domains with one being time-series, financial data and the other being wine data. As hypothesized, poorly constructed, or bad knowledge, has no effect while good knowledge is shown to have a significant effect. Bad knowledge, being random in character in these experiments, has understandably no impact on an already random mutation process. However, employing knowledge to guide the mutation process significantly constrains the traversal of the search space. Employing knowledge in an evolutionary algorithm has the potential to increase the efficiency and accuracy of evolutionary algorithms.
AB - Abstract Good knowledge would be expected to help a knowledge-based algorithm more than bad knowledge. In this research, the precise effect of good versus bad knowledge on evolutionary algorithms is explored. The testable hypothesis of this paper is that good knowledge will have a significant effect on the evolutionary mutation process, whereas bad knowledge will have no significant effect. A knowledge-guided evolutionary algorithm is developed where ontologies, representing knowledge, are applied to the mutation process. Bad knowledge is represented as a randomly generated ontology, while good knowledge is represented by ontologies constructed with domain knowledge and following a formal ontology development process. Decision trees are evolved to solve a classification problem. Fitness is classification accuracy. The experiment is replicated over 2 data-sets from different domains with one being time-series, financial data and the other being wine data. As hypothesized, poorly constructed, or bad knowledge, has no effect while good knowledge is shown to have a significant effect. Bad knowledge, being random in character in these experiments, has understandably no impact on an already random mutation process. However, employing knowledge to guide the mutation process significantly constrains the traversal of the search space. Employing knowledge in an evolutionary algorithm has the potential to increase the efficiency and accuracy of evolutionary algorithms.
KW - Decision trees
KW - Evolutionary algorithms
KW - Genetic algorithm
KW - Knowledge guided
KW - Ontology
UR - https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/21
UR - https://doi.org/10.1016/j.eswa.2015.04.064
U2 - 10.1016/j.eswa.2015.04.064
DO - 10.1016/j.eswa.2015.04.064
M3 - Article
SN - 0957-4174
VL - 42
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -