Abstract. In this paper we investigate the properties of CEAs with populations structured as Watts–Strogatz small-world graphs and Albert–Barabási scale-free graphs as problem solvers, using several standard discrete optimization problems as a benchmark. The EA variants employed include self-adaptation of mutation rates. Results are compared with the corresponding classical panmictic EA show-ing that topology together with self-adaptation drastically influences the search. 1

Abstract. 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 scale logarithmically with respect to the problem size, which makes it suitable to tackle large instances. 1

Abstract This paper studies the scalability of an Evolutionary Algorithm (EA) whose population is structured by means of a gossiping protocol and where the evolutionary operators act exclusively within the local neighborhoods. This makes the algorithm inherently suited for parallel execution in a peer-to-peer fashion which, in turn, offers great advantages when dealing with computationally expensive problems because distributed execution implies massive scalability. In this paper we show another advantage of this algorithm: We experimentally demonstrate that it scales up better than traditional alternatives even when executed in a sequential fashion. In particular, we analyze the behavior of several EAs on wellknown deceptive trap functions with varying sizes and levels of deceptiveness. The results show that the new EA requires smaller optimal population sizes and fewer fitness evaluations to reach solutions. The relative advantage of the new EA is more outstanding as problem hardness and size increase. In some cases the new algorithm reduces the computational efforts of the traditional EAs by several orders of magnitude. Keywords Peer-to-peer computing Evolutionary algorithms