On the Length of Programs for Computing Finite Binary Sequences
Journal of the ACM (JACM)
On the Length of Programs for Computing Finite Binary Sequences: statistical considerations
Journal of the ACM (JACM)
Kolmogorov's Structure Functions with an Application to the Foundations of Model Selection
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Handbook of Computational Economics)
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Design of evolutionary algorithms-A statistical perspective
IEEE Transactions on Evolutionary Computation
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Collective specialization in multi-rover systems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Three interconnected parameters for genetic algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Optimisation and generalisation: footprints in instance space
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Parameter tuning of evolutionary algorithms: generalist vs. specialist
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Meta-optimization for parameter tuning with a flexible computing budget
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
Automatic (offline) configuration of algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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We present and evaluate a method for estimating the relevance and calibrating the values of parameters of an evolutionary algorithm. The method provides an information theoretic measure on how sensitive a parameter is to the choice of its value. This can be used to estimate the relevance of parameters, to choose between different possible sets of parameters, and to allocate resources to the calibration of relevant parameters. The method calibrates the evolutionary algorithm to reach a high performance, while retaining a maximum of robustness and generalizability. We demonstrate the method on an agent-based application from evolutionary economics and show how the method helps to design an evolutionary algorithm that allows the agents to achieve a high welfare with a minimum of algorithmic complexity.