Asynchronous parallel search by the parallel genetic algorithm

The parallel genetic algorithm (PGA) is a prototype of a new kind of a distributed algorithm. It is based on a parallel search by individuals all of which have the complete problem description. The information exchange between the individuals is done by simulating biological principles of evolution. The PGA is totally asynchronous, running with maximal efficiency on MIMD parallel computers. The search strategy of the PGA is based on a small number of intelligent and active individuals, whereas a GA uses a large population of passive individuals. The author shows the power of the PGA with two combinatorial problems-the graph partitioning problem and the autocorrelation problem. In these examples, the PGA has found solutions of very large problems, which are comparable or even better than any other solution found by other heuristics.<
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