What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
The evolution of evolvability in genetic programming
Advances in genetic programming
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Neutrality in fitness landscapes
Applied Mathematics and Computation
Properties of fitness functions and search landscapes
Theoretical aspects of evolutionary computing
How to detect all maxima of a function
Theoretical aspects of evolutionary computing
On classifications of fitness functions
Theoretical aspects of evolutionary computing
Fitness landscapes and evolvability
Evolutionary Computation
Hyperplane Ranking in Simple Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Epistasis in Genetic Algorithms: An Experimental Design Perspective
Proceedings of the 6th International Conference on Genetic Algorithms
A Bit-Wise Epistasis Measure for Binary Search Spaces
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
The Effect of Spin-Flip Symmetry on the Performance of the Simple GA
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An Analysis of the Configuration Space of the Maximal Constraint Satisfaction Problem
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Searching in the Presence of Noise
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
The Density of States - A Measure of the Difficulty of Optimisation Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application
Advances in evolutionary computing
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic algorithms
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
Complexity Theory: Exploring the Limits of Efficient Algorithms
Complexity Theory: Exploring the Limits of Efficient Algorithms
Information Characteristics and the Structure of Landscapes
Evolutionary Computation
PSO and multi-funnel landscapes: how cooperation might limit exploration
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The dispersion metric and the CMA evolution strategy
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Deceptiveness and neutrality the ND family of fitness landscapes
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A quantitative study of neutrality in GP boolean landscapes
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Simulated annealing applied to test generation: landscape characterization and stopping criteria
Empirical Software Engineering
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary Computation
A study of NK landscapes' basins and local optima networks
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Problem difficulty analysis for particle swarm optimization: deception and modality
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Analyzing the landscape of a graph based hyper-heuristic for timetabling problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Quantifying ruggedness of continuous landscapes using entropy
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Decomposition of fitness functions in random heuristic search
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
A comprehensive view of fitness landscapes with neutrality and fitness clouds
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Adapting to complexity during search in combinatorial landscapes
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
The detrimentality of crossover
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
NK landscapes, problem difficulty, and hybrid evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Fitness-probability cloud and a measure of problem hardness for evolutionary algorithms
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
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
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Fitness landscapes and graphs: multimodularity, ruggedness and neutrality
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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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Real-world optimisation problems are often very complex. Metaheuristics have been successful in solving many of these problems, but the difficulty in choosing the best approach can be a huge challenge for practitioners. One approach to this dilemma is to use fitness landscape analysis to better understand problems before deciding on approaches to solving the problems. However, despite extensive research on fitness landscape analysis and a large number of developed techniques, very few techniques are used in practice. This could be because fitness landscape analysis in itself can be complex. In an attempt to make fitness landscape analysis techniques accessible, this paper provides an overview of techniques from the 1980s to the present. Attributes that are important for practical implementation are highlighted and ways of adapting techniques to be more feasible or appropriate are suggested. The survey reveals the wide range of factors that can influence problem difficulty, emphasising the need for a shift in focus away from predicting problem hardness towards measuring characteristics. It is hoped that this survey will invoke renewed interest in the field of understanding complex optimisation problems and ultimately lead to better decision making on the use of appropriate metaheuristics.