Virus-evolutionary genetic algorithm for a self-organizing manufacturing system

Abstract

A virus-evolutionary genetic algorithm (VEGA) based on virus theory of evolution is proposed. The VEGA is composed of a host population of candidate solutions and a virus population of substrings of host individuals. Two new operators are introduced: (1) a reverse transcription operator which overwrites a virus' string on a host's string and, (2) a transduction operator generating a new virus from a host string. In this paper, the VEGA is applied to the traveling salesman problem. The VEGA is also applied to a pallet location problem of a press machining line in a self-organizing manufacturing system, in which a process effectively self-organizes according to other processes. Simulation results show the effectiveness of the proposed algorithm and that the virus population possesses effective schemata.