Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Adaptive Retrieval Agents: Internalizing Local Contextand Scaling up to the Web
Machine Learning - Special issue on information retrieval
Hierarchical federations: an architecture for information hiding
Proceedings of the fifteenth workshop on Parallel and distributed simulation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Trends in Cooperative Distributed Problem Solving
IEEE Transactions on Knowledge and Data Engineering
Efficient and Scalable Pareto Optimization by Evolutionary Local Selection Algorithms
Evolutionary Computation
Synchroscalar: Evaluation of an embedded, multi-core architecture for media applications
Journal of Embedded Computing - Issues in embedded single-chip multicore architectures
AgentScapes: designing water efficient landscapes using distributed agent-based optimization
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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The facility location problem also known as p-median problem concerns the positioning of facilities such as bus-stops, broadcasting stations or supply stations in general. The objective is to minimize the weighted distance between demand points (or customers) and facilities. In general there is a trend towards networked and distributed organizations and their systems, complicating the design, construction and maintenance of distributed facilities as information is scattered among participants while no global view exists. There is a need to investigate distributed approaches to the p-median problem. This paper contributes to research on location problems by proposing an agent oriented decentralized evolutionary computation (EC) approach that exploits the flow of money or energy in order to realize distributed optimization. Our approach uses local operators for reproduction like mutation, recombination and selection finally regulated by market mechanisms. This paper presents two general outcomes of our model: how adaptation occurs in the number and strategies of agents leading to an improvement at the system level. The novelty of this approach lies in the biology-inspired bottom-up adaptation method for inherent distributed problems. It is applied to the uncapacitated p-median problem but is also intended to be general for a wide variety of problems and domains, e.g. wireless sensor networks.