# A Survey of Advances in Landscape Analysis for Optimisation
## 3 Advances in Landscape Analysis
### Techniques for Landscape Analysis  
An earlier survey of fitness landscapes in evolutionary computation [1] identified 22 techniques for analysing landscapes, from genetic algorithm (GA)-deception proposed by [PERSON] in 1987 [21] to accumulated escape probability by [PERSON] and coauthors in 2011 [22]. The aim of the original survey was to make the techniques more accessible to researchers by describing each technique in an understandable way and highlighting attributes affecting their implementation in practice. Table 1 continues where the previous survey left off, introducing a further 11 techniques starting with technique 23 as local optima networks (LONs) [23]. Although LONs were mentioned in the previous survey as a model for describing the structure of landscapes, they were not listed as a practical technique for analysis. Since then the LON model has been extended and has evolved into one of the most widely used landscape analysis techniques today. The techniques in Table 1 appear in chronological order by the year of the first publication and are described under the following headings:  
* _Technique_ #: the name of the technique, citation and extensions (where the technique was adapted in subsequent studies). * _Year_: the year the technique was first introduced in published form. * _Focus_: refers to what is measured or predicted by the technique. * _Assumptions_: any significant assumptions on which the technique is based. * _Description_: summary of how the technique works. * _Result_: describes the form of output produced by the technique (numerical, graphical, etc.). When the original list of landscape analysis techniques was compiled, an attempt to classify the techniques was unsuccessful. This was because the possible dimensions on which to base a classification did not help to distinguish between techniques. For example, consider a distinction between local/global or exact/approximate landscape analysis techniques. Many techniques did not fit into either of these hard classes. Even if they did fit into one class, small adaptations would move them into a different class. Instead of a classification of techniques, the approach used was to highlight distinguishing features to assist practitioners in deciding which approaches could be applicable in different scenarios. The choice of an appropriate technique (or set of techniques) to use should be guided by the nature of the problems to be analysed as well as the purpose of the analysis. The _Assumptions_ field in Table 1 specifies whether the technique could be applied to the given problems, while the _Focus_ and _Result_ fields specify whether the aim is likely to be met by the technique. For example, say your aim is to understand why a particular algorithm is failing on a vehicle routing problem (a combinatorial problem). Technique 23 (LONs) could be applicable, since it is designed for discrete search spaces and has a focus on the global structure of landscapes, which is known to affect search behaviour. LONs could be generated for a range of problems to contrast the global structure of problems on which the algorithm was successful and those on which it failed, to hopefully shed light on possible explanations for why the algorithm is failing. In a similar way, Techniques 25, 29 (and 31 if the algorithm was population-based) could be used to provide a different view of the problem focusing on other aspects namely variations in gradients, local fitness patterns (and evolvability), respectively. Technique 28 would only be appropriate if the problem was modelled as a constrained problem, while Technique 32 would only be appropriate if the problem was modelled as a multiobjective problem. Techniques 24, 27 and 30 would not be applicable because they assume a continuous search space, and Techniques 26 and 33 would also not be appropriate because the problem scenarios are different (coevolution and neural networks, respectively).