Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Biases in the crossover landscape
Proceedings of the third international conference on Genetic algorithms
The evolution of evolvability in genetic programming
Advances in genetic programming
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Towards an Optimal Mutation Probability for Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Fitness Distance Correlation And Problem Difficulty For Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Metric for Genetic Programs and Fitness Sharing
Proceedings of the European Conference on Genetic Programming
Peptide detectability following ESI mass spectrometry: prediction using genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Negative slope coefficient: a measure to characterize genetic programming fitness landscapes
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Diversity in genetic programming: an analysis of measures and correlation with fitness
IEEE Transactions on Evolutionary Computation
The Role of Population Size in Rate of Evolution in Genetic Programming
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Have your spaghetti and eat it too: evolutionary algorithmics and post-evolutionary analysis
Genetic Programming and Evolvable Machines
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The main aim of landscape analysis has been to quantify the 'hardness' of problems. Early steps have been made towards extending this into Genetic Programming. However, few attempts have been made to extend the use of landscape analysis into the prediction of ways to make a problem easy, through the optimal setting of control parameters. This paper introduces a new class of landscape metrics, which we call 'Genotype-Fitness Correlations'. An example of this family of metrics is applied to six real-world regression problems. It is demonstrated that genotype-fitness correlations may be used to estimate optimum population sizes for the six problems. We believe that this application of a landscape metric as guidance in the setting of control parameters is an important step towards the development of an adaptive algorithm that can respond to the perceived landscape in 'real-time', i.e. during the evolutionary search process itself.