Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
A self-adaptive migration model genetic algorithm for data mining applications
Information Sciences: an International Journal
Adaptive operator selection with dynamic multi-armed bandits
Proceedings of the 10th annual conference on Genetic and evolutionary computation
EvAg: a scalable peer-to-peer evolutionary algorithm
Genetic Programming and Evolvable Machines
Toward comparison-based adaptive operator selection
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The benefit of migration in parallel evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
General scheme for analyzing running times of parallel evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Using self-adaptable probes for dynamic parameter control of parallel evolutionary algorithms
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
A dynamic island model for adaptive operator selection
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Autonomous local search algorithms with island representation
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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We present a distributed algorithm, Select Best and Mutate (SBM), in the Distributed Adaptive Metaheuristic Selection (DAMS) framework. DAMS is dedicated to adaptive optimization in distributed environments. Given a set of metaheuristics, the goal of DAMS is to coordinate their local execution on distributed nodes in order to optimize the global performance of the distributed system. DAMS is based on three-layer architecture allowing nodes to decide distributively what local information to communicate, and what metaheuristic to apply while the optimization process is in progress. SBM is a simple, yet efficient, adaptive distributed algorithm using an exploitation component allowing nodes to select the metaheuristic with the best locally observed performance, and an exploration component allowing nodes to detect the metaheuristic with the actual best performance. SBM features are analyzed from both a parallel and an adaptive point of view, and its efficiency is demonstrated through experimentations and comparisons with other adaptive strategies (sequential and distributed).