Self-adaptation in evolving systems
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms
Journal of Heuristics
Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial
Journal of Heuristics
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
An Empirical Study on GAs "Without Parameters"
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Optimal Resource-Aware Deployment Planning for Component-Based Distributed Applications
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Evolving car designs using model-based automated safety analysis and optimisation techniques
Journal of Systems and Software - Special issue: Computer software & applications
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
AORCEA - An adaptive operator rate controlled evolutionary algorithm
Computers and Structures
Software deployment architecture and quality-of-service in pervasive environments
International workshop on Engineering of software services for pervasive environments: in conjunction with the 6th ESEC/FSE joint meeting
A user-centric approach for improving a distributed software system's deployment architecture
A user-centric approach for improving a distributed software system's deployment architecture
Analysis of adaptive operator selection techniques on the royal road and long k-path problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
ArcheOpterix: An extendable tool for architecture optimization of AADL models
MOMPES '09 Proceedings of the 2009 ICSE Workshop on Model-Based Methodologies for Pervasive and Embedded Software
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Limitations of existing mutation rate heuristics and how a rank GA overcomes them
IEEE Transactions on Evolutionary Computation
Let the Ants Deploy Your Software - An ACO Based Deployment Optimisation Strategy
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
Reliability-driven deployment optimization for embedded systems
Journal of Systems and Software
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Idealized dynamic population sizing for uniformly scaled problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Optimizing resource usage in component-based real-time systems
CBSE'05 Proceedings of the 8th international conference on Component-Based Software Engineering
A decentralized redeployment algorithm for improving the availability of distributed systems
CD'05 Proceedings of the Third international working conference on Component Deployment
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
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Evolutionary Algorithms are equipped with a range of adjustable parameters, such as crossover and mutation rates which significantly influence the performance of the algorithm. Practitioners usually do not have the knowledge and time to investigate the ideal parameter values before the optimisation process. Furthermore, different parameter values may be optimal for different problems, and even problem instances. In this work, we present a parameter control method which adjusts parameter values during the optimisation process using the algorithm's performance as feedback. The approach is particularly effective with continuous parameter intervals, which are adapted dynamically. Successful parameter ranges are identified using an entropy-based clusterer, a method which outperforms state-of-the-art parameter control algorithms.