Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
On classifications of fitness functions
Theoretical aspects of evolutionary computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Fitness landscapes and evolvability
Evolutionary Computation
Evolutionary algorithms for the satisfiability problem
Evolutionary Computation
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
On the Optimization of Monotone Polynomials by Simple Randomized Search Heuristics
Combinatorics, Probability and Computing
Information landscapes and problem hardness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Complexity Theory: Exploring the Limits of Efficient Algorithms
Complexity Theory: Exploring the Limits of Efficient Algorithms
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
How mutation and selection solve long-path problems in polynomial expected time
Evolutionary Computation
Worst-case and average-case approximations by simple randomized search heuristics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
About the Computation Time of Adaptive Evolutionary Algorithms
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Real options approach to evaluating genetic algorithms
Applied Soft Computing
A pheromone-rate-based analysis on the convergence time of ACO algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Visual exploration of algorithm parameter space
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Quantifying ruggedness of continuous landscapes using entropy
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Global characterization of the CEC 2005 fitness landscapes using fitness-distance analysis
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
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
Evolutionary algorithm characterization in real parameter optimization problems
Applied Soft Computing
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
Information Sciences: an International Journal
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Various methods have been defined to measure the hardness of a fitness function for evolutionary algorithms and other black-box heuristics. Examples include fitness landscape analysis, epistasis, fitness-distance correlations etc., all of which are relatively easy to describe. However, they do not always correctly specify the hardness of the function. Some measures are easy to implement, others are more intuitive and hard to formalize. This paper rigorously defines difficulty measures in black-box optimization and proposes a classification. Different types of realizations of such measures are studied, namely exact and approximate ones. For both types of realizations, it is proven that predictive versions that run in polynomial time in general do not exist unless certain complexity-theoretical assumptions are wrong.