One Operator, One Landscape

The use of the term “landscape” is increasing rapidly in the field of evolutionary computation, yet in many cases it remains poorly, if at all, defined. This situation has perhaps developed because everyone grasps the imagery immediately, and the questions that would be asked of a less evocative term do not get asked. This paper presents an important consequence of a new model of landscapes. The model is general enough to encompass most of what computer scientists would call search, though it is not restricted to either the field or the viewpoint. The consequence is a “one-operator, one-landscape” view of search algorithms that is particularly relevant for algorithms that search via the use of multiple operators, and hence to genetic algorithms and other members of the evolutionary computing family. Crossover and selection landscapes are presented as siblings of the traditional mutation landscape. The model encourages a perspective on search algorithms that makes a clear division between landscape structures and navigation upon them. This division is the basis for the design of new search algorithms that combine elements of existing algorithms, an example of which is a crossover hillclimber. The model also establishes a strong connection withthe heuristic state space search algorithms of Artificial Intelligence.