Evaluating Island-based EAs on Unstable Networks with Complex Failure Patterns
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The document discusses the evaluation of island-based evolutionary algorithms (EAs) on unstable networks characterized by complex failure patterns. It highlights the use of parallel and distributed models to enhance solution quality and addresses challenges posed by dynamic environments with churn and correlated failures. Key concepts include self-scaling and self-sampling to improve algorithm resilience and adaptability in the face of node instability.
Evaluating Island-based EAs on Unstable Networks with Complex Failure Patterns
- 1. Evaluating Island-based EAs on Unstable Networks with Complex Failure Patterns Rafael Nogueras & Carlos Cotta Dpto. Lenguajes y Ciencias de la Computación, Universidad de Málaga GECCO 2017 Berlin EphemeCH TIN2014-56494-C4-1-P Use of parallel and distributed models of EAs to improve solution quality and reduce computational times. The island model spatially organizes populations into partially isolated panmictic demes. Two emergent computational environments: P2P networks and desktop grids.They are dynamic and unstable. Churn: the combined effect of multiple computing nodes leaving and entering the system along time. Scale-free topology (Barabási-Albert model) Platforms with many nodes Instability FaultTolerance • Unstable scenarios with correlated failures pose a hard challenge. • Self-✭ properties are an effective solution. • Self-scaling and self-sampling are robust under different failure patterns. FutureWork Extend this work on other models of correlated failures and network topologies. Self-Scaling: the algorithm changes its structure in response to variations in the problem or environment. Islands dynamically change their size in the presence of churn: • If a neighboring island goes down, an island increases its size. • Active islands exchange individuals in order to balance their sizes (self-balancing). Self-Sampling: Prevent randomness when an island increases its size ⇒ EDAs to estimate the population of each island to be enlarged generating new individuals by sampling. Node failures are influenced by neighboring nodes ⇒ sandpile model variant. A micro-failure event happens with probability p(t): when the number of such micro-failures equals the number of active neighbors ⇒ the node is disconnected from the system. Reactivation doesn’t change. Each node can switch from active to inactive or vice versa independently of other nodes with some probability p(t) that depends on the time it has been in its current state and follows a Weibull distribution. TRAP HIFF MMDP Scale-free networks feature a power-law distribution in node degrees.These networks are often the result of processes driven by preferential attachment.