Coding and information theory
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application
Advances in evolutionary computing
Information landscapes and problem hardness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Information Characteristics and the Structure of Landscapes
Evolutionary Computation
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
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
Optimization in Fractal and Fractured Landscapes Using Locust Swarms
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
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
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
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A major unsolved problem in the field of optimisation and computational intelligence is how to determine which algorithms are best suited to solving which problems. This research aims to analytically characterise individual problems as a first step towards attempting to link problem types with the algorithms best suited to solving them. In particular, an information theoretic technique for analysing the ruggedness of a fitness landscape with respect to neutrality was adapted to work in continuous landscapes and to output a single measure of ruggedness. Experiments run on test functions with increasing ruggedness show that the proposed measure of ruggedness produced relative values consistent with a visual inspection of the problem landscapes. Combined with other measures of complexity, the proposed ruggedness measure could be used to more broadly characterise the complexity of fitness landscapes in continuous domains.