What makes an optimization problem hard?
Complexity
Ruggedness and neutrality—the NKp family of fitness landscapes
ALIFE Proceedings of the sixth international conference on Artificial life
Neutrality in fitness landscapes
Applied Mathematics and Computation
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Information Characteristics and the Structure of Landscapes
Evolutionary Computation
Computers and Operations Research
Evolutionary Computation
Algebraic theory of recombination spaces
Evolutionary Computation
Problem difficulty analysis for particle swarm optimization: deception and modality
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Multistart tabu search and diversification strategies for the quadratic assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Decomposition of fitness functions in random heuristic search
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Elementary landscape decomposition of the quadratic assignment problem
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Automatic algorithm selection for the quadratic assignment problem using fitness landscape analysis
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
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Fitness landscape analysis methods have become an increasingly popular topic for research. The future application of these methods to metaheuristics can yield advanced self-adaptive metaheuristics and knowledge bases that can take the role of expert systems in the field of optimization. One important feature of such an expert system would be the prediction of algorithm effort on a certain instance. Estimating whether a certain algorithm is able to tackle the problem adequately or not is a valuable piece of information that currently only an experienced human expert can give. The ability to generate such an advice automatically is, therefore, an important milestone. While fitness landscape analysis methods have been developed for exactly this purpose, it has been shown in the past that single-value analyses have limited applicability. Here, a general method for extracting fitness landscape features will be shown in combination with regression models that indicate a strong correlation between the actual and the predicted effort. Significant potential to increase the prediction quality arises when combining several measures each derived from several different sampling trajectories.