# A Survey of Advances in Landscape Analysis for Optimisation
## 4 Applications of Landscape Analysis
### Automated Algorithm Selection  
Performance complementarity is a phenomenon where different algorithms perform the best on different types of problem instances [118]. Automated algorithm selection is the process of deciding on the best algorithm for the problem at hand and depends on having features for distinguishing problems from each other. The features can be problem specific but can also be derived through landscape analysis, which can be more generally applied across problem types.  
Algorithm configuration (choosing of algorithm parameters and strategies for particular problems) is closely related to algorithm selection but differs in that the set of possible configurations is much larger than a finite choice of algorithm candidates. The features used for algorithm configuration and selection can, however, be the same, so studies related to algorithm configuration are also mentioned here. Examples of algorithms that have been configured or dynamically adapted based on landscape characteristics include:  
* genetic algorithms: using the fitness distance correlation landscape measure to dynamically adjust the migration period in a distributed genetic algorithm [119], selecting a crossover operator based on fitness landscape properties [120], using fitness landscape features to estimate the optimal population size [121];
* differential evolution algorithms: adapting the strategy and adjusting the control parameters based on detected landscape modality [122; 123], adapting the mutation strategy based on landscape features [124; 125], algorithm configuration based on exploratory landscape features with an empirical performance model [126];
* memetic algorithms: analysis of the separability of problems to automatically select operators [45] and the use of four fitness landscape analysis techniques to inform the most suitable crossover operator [127];
* selection of CMA-ES algorithm configuration using a trained model for predicting performance based on landscape features that was shown to outperform the default setting of CMA-ES [128];
* surrogate-assisted particle swarm optimisation, where fitness landscape analysis was used to select surrogate models [129]; and
* decomposition-based multiobjective evolutionary algorithms (MOEA/D), where the addition of landscape information improved the behaviour of the adaptive operator selection mechanism [130].  
There are many attempts at automated algorithm selection using landscape features and the reader is referred to a survey by [PERSON] et al. [118] for an overview of studies. Recent contributions not included in the [PERSON] et al. survey include algorithm selection for the quadratic assignment problem [131; 132], algorithm selection for the travelling salesman problem [133], algorithm selection for the permutation flowshop problem [134] and a form of automated algorithm selection for constraint handling techniques with differential evolution [61].  
From the above, it is clear that landscape analysis is playing an important role in both algorithm performance prediction and automated algorithm configuration and selection.