An adaptive parallel genetic algorithm system for i-computing environment

Abstract

Many real-world optimization problems in the scientific and engineering fields can be solved by genetic algorithms (GAs) but it still requires a long execution time for complex problems. At the same time, there are many under-utilized workstations on the Internet. In this paper, we present a self-adaptive parallel GA system named APGAIN, which utilizes the spare power of the heterogeneous workstations on the Internet to solve complex optimization problems. In order to maintain a balance between exploitation and exploration, we have devised a novel probabilistic rule-driven adaptive model (PRDAM) to adapt the GA parameters automatically. APGAIN is implemented on an Internet Computing system called DJM. In the implementation, we discover that DJM's original load balancing strategy is insufficient. Hence the strategy is extended with the job migration capability. The performance of the system is evaluated by solving the traveling salesman problem with data from a public database.