Adapting evolutionary algorithms to the concurrent functional language Erlang
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Efficient training set use for blood pressure prediction in a large scale learning classifier system
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper explores the scalability and performance of pool and island based evolutionary algorithms, both of them using as a mean of interaction an object store, we call this family of algorithms SofEA. This object store allows the different clients to interact asynchronously, the point of the creation of this framework is to build a system for spontaneous and voluntary distributed evolutionary computation. The fact that each client is autonomous leads to a complex behavior that will be examined in the work, so that the design can be validated, rules of thumb can be extracted, and the limits of scalability can be found. In this paper we advance the design of an asynchronous, fault-tolerant and scalable distributed evolutionary algorithm based on the object store CouchDB. We test experimentally the different options and show the trade-offs that pool and island-based solutions offer.