Translation of the Mutation Operator from Genetic Algorithms to Evolutionary Ontologies

Diana Contras,Oliviu Matei

doi:10.14569/ijacsa.2016.070186

Abstract

Recently introduced, evolutionary ontologies rep-resent a new concept as a combination of genetic algorithms and ontologies. We have defined a new framework comprising a set of parameters required for any evolutionary algorithm, i.e. ontological space, representation of individuals, the main genetic operators such as selection, crossover, and mutation. Although a secondary operator, mutation proves its importance in creating and maintaining evolutionary ontologies diversity. Therefore, in this article, we widely debate the mutation topic in evolutionary ontologies, marking its usefulness in practice by experimental results. Also we introduce a new mutation operator, called relational mutation, concerning mutation of a relationship through its inverse.

Highlights

Evolutionary ontologies (EO) reprezent a new concept, introduced very recently by Matei et al in [1]
Originated in classical genetic operators, crossover and mutation that are used in evolutionary ontologyies are new operators designed to meet the needs of complex structuring of knowledge contained in ontologies
An individual of the evolutionary ontologies is a subset of the ontology, meaning where ninst is the number of instances in the ontology

Summary

INTRODUCTION

Evolutionary ontologies (EO) reprezent a new concept, introduced very recently by Matei et al in [1]. EO are genetic algorithms using ontologies as individual items instead of classical data structures such as strings of bits, different values (real numbers, characters, objects) or programs. In EO is followed the pattern of the evolutionary process: it is selected an initial population that undergoes genetic operators such as crossover and mutation, the offsping are subjected again to selection for resuming the algorithm until the condition of the problem to be solved is fulfilled or the maximum number of epochs is reached. Originated in classical genetic operators, crossover and mutation that are used in evolutionary ontologyies are new operators designed to meet the needs of complex structuring of knowledge contained in ontologies. Due to the complex nature of individuals, is required the instantiation mutation, resulting three types of mutation operators, namely class mutation, instance mutation and property mutation

Class mutation

MUTATION IN EVOLUTIONARY ONTOLOGIES

Instance mutation

Data Property Mutation

Findings

CONCLUSION