Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Zen of code optimization
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
An introduction to genetic algorithms
An introduction to genetic algorithms
From wetware to software: a cybernetic perspective of self-adaptive software
IWSAS'01 Proceedings of the 2nd international conference on Self-adaptive software: applications
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Inspired by the autonomic aspects of the human central nervous system, the vision of “autonomiccomputing” arrived with a fully-formed wish list of characteristics that such systems should exhibit, essentially those self-referential aspects required for effective self-management. Here, the authors contend that the biologically-inspired managerial cybernetics of Beer’s Viable System Model (VSM) provides significant conceptual guidance for the development of a general architecture for the operation and management of such complex, evolving, adaptive systems. Consequently, the VSM has been used as the basis of a theoretically-supported reference model that provides the "blueprint" for an extensible intelligent agent architecture. Of course, normal use of the VSM relies heavily on human agency to realize the adaptive capabilities required by the model. Therefore, artificially replicating such activities represents a significant challenge, however the authors show that some progress can be made using algorithmic hot swapping and in particular Holland’s Genetic Algorithms (GA’s) to generate, in specific circumstances, a repertoire of tailored responses to environmental change. The authors then speculate on the use of the associated Learning Classifier Systems (LCS) approach to allow the system to develop an adaptive environmental model of appropriate, optimized responses.