Simulated annealing: theory and applications
Simulated annealing: theory and applications
Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms in Engineering Applications
Evolutionary Algorithms in Engineering Applications
Telecommunications Optimization: Heuristic and Adaptive Computation Techniques
Telecommunications Optimization: Heuristic and Adaptive Computation Techniques
Allocating Data and Operations to Nodes in Distributed Database Design
IEEE Transactions on Knowledge and Data Engineering
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Nature-Inspired Computing Technology and Applications
BT Technology Journal
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
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Evolutionary algorithms have been shown to be effective in providing configuration optimisation to dynamic load balancing in distributed database systems and Web servers. This paper explores the tuning parameter performance profile of such techniques over a variety of problems, including the adaptive distributed database management problem (ADDMP), focusing on a range of interesting and important features. The ability of the evolutionary search process to reliably find good solutions to a dynamic problem in a minimal and consistent run-time is of paramount importance when considering their application to real-time industrial control problems. This paper demonstrates the existence of certain optimal parameter values, particularly for the rate of applied mutation, which are shown to produce consistently good problem solutions in a low number of evaluations with a minimum standard deviation.