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In this paper we present a distributed Evolutionary Algo- rithm (EA) whose population is structured using newscast, a gossiping protocol. This algorithm has been designed to deal with computation- ally expensive problems via massive scalability; therefore, we analyse the response time of the model using large instances of well-known hard op- timization problems that require from EAs a (sometimes exponentially) bigger computational eort as these problems scale. Our approach has been matched against a sequential Genetic Algorithm (sGA) applied to the same set of problems, and we found that it needs less computational eort than the sGA in yielding success. Furthermore, the response time scale logarithmically with respect to the problem size, which makes it suitable to tackle large instances.

P2P Evolutionary Algorithms: A Suitable Approach for Tackling Large Instances in Hard Optimization Problems