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

In this paper we present a distributed Evolutionary Algorithm (EA) whose population is structured using newscast, a gossiping protocol. This algorithm has been designed to deal with computationally expensive problems via massive scalability; therefore, we analyse the response time of the model using large instances of well-known hard optimization problems that require from EAs a (sometimes exponentially) bigger computational effort 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 effort than the sGA in yielding success. Furthermore, the response time scales logarithmically with respect to the problem size, which makes it suitable to tackle large instances.