Optimization of globally convex functions
SIAM Journal on Control and Optimization
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Memetic Algorithms and the Fitness Landscape of the Graph Bi-Partitioning Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
GA Based on the UV-Structure Hypothesis and Its Application to JSP
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic algorithms
The dispersion metric and the CMA evolution strategy
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The Impact of Global Structure on Search
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An empirical comparison of parallel and distributed particle swarm optimization methods
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Why six informants is optimal in PSO
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A call for collaborative landscape analysis
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
The lay of the land: a brief survey of problem understanding
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A meta-learning prediction model of algorithm performance for continuous optimization problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Length scale for characterising continuous optimization problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Recent advances in problem understanding: changes in the landscape a year on
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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We interpret real-valued black-box optimization problems over continuous domains as black-box landscapes. The performance of a given optimization heuristic on a given problem largely depends on the characteristics of the corresponding landscape. Designing statistical measures that can be used to classify landscapes and quantify their topographical properties is hence of great importance. We transfer the concept of fitness-distance analysis from theoretical biology and discrete combinatorial optimization to continuous optimization and assess its potential to characterize black-box landscapes. Using the CEC 2005 benchmark functions, we empirically test the robustness and accuracy of the resulting landscape characterization and illustrate the limitations of fitness-distance analysis. This provides a first step toward a classification of real-valued black-box landscapes over continuous domains.