A Framework for Distributed Evolutionary Algorithms

Lecture Notes in Computer Science

Eiben

Steen

et al. 2008

Self Cite

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.

Section: Evolvable Agentmentioning

confidence: 99%

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

Lecture Notes in Computer Science

Eiben

Steen

et al. 2008

Self Cite

“…P2P systems, on the other hand, overcome this issue with a decentralized architecture in which ev-ery computing node acts either as a client or as a server, thus limiting the impact a node can have on the system. Following such a decentralized philosophy, many approaches have been tackling the design of P2P meta-heuristics [Arenas et al(2002), Wickramasinghe et al(2007), , Biazzini and Montresor(2010), Scriven et al(2009), Bánhelyi et al(2009), Biazzini and Montresor(2013]. …”

Section: Related Workmentioning

confidence: 99%

Designing robust volunteer-based evolutionary algorithms

Genet Program Evolvable Mach

Bouvry

González³

et al. 2014

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This paper tackles the design of scalable and fault-tolerant evolutionary algorithms computed on volunteer platforms. These platforms aggregate computational resources from contributors all around the world. Given that resources may join the system only for a limited period of time, the challenge of a volunteer-based evolutionary algorithm is to take advantage of a large amount of computational power that in turn is volatile. The paper analyzes first the speed of convergence of massively parallel evolutionary algorithms. Then, it provides some guidance about how to design efficient policies to overcome the algorithmic loss of quality when the system undergoes high rates of transient failures, i.e. computers fail only for a limited period of time and then become available again. In order to provide empirical evidence, experiments were conducted for two well-known problems which require large population sizes to be solved, the first based on a genetic algorithm and the second on genetic programming. Results show that, in general, evolutionary algorithms undergo a graceful degradation under the stress of losing computing nodes. Additionally, new available nodes can also contribute to improving the search process. Despite losing up to 90% of the initial computing resources, volunteer-based evolutionary algorithms can find the same solutions in a failure-prone as in a failure-free run.

“…-The DREAM project, [3], is one of the pioneering frameworks for P2P Evolutionary Computation (EC). The project focuses on the distributed processing of EAs and uses the P2P engine DRM (Distributed Resource Machine) which is an implementation of the newscast protocol [19].…”

Section: Related Workmentioning

confidence: 99%

“…This way future works could easily extend the EvAg definition (i.e., behavioral learning between agents, self-adaptive population size adjustment during run-time [39] or load balancing mechanisms within a real network [3]). Table 1 shows the pseudo-code of an EvAg where the agent owns an evolving solution (S actual ).…”

Section: Evolvable Agentmentioning

confidence: 99%

EvAg: a scalable peer-to-peer evolutionary algorithm

Genet Program Evolvable Mach

Eiben

Steen

et al. 2009

Self Cite

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.