Fitness landscape

The idea of a fitness landscape is a metaphor to help explain flawed forms in evolution by natural selection, including exploits and glitches in animals like their reactions to supernormal stimuli.

The idea of studying evolution by visualizing the distribution of fitness values as a kind of landscape was first introduced by Sewall Wright in 1932.

An evolving population typically climbs uphill in the fitness landscape, by a series of small genetic changes, until – in the infinite time limit – a local optimum is reached.

[3] Hard landscapes are characterized by the maze-like property by which an allele that was once beneficial becomes deleterious, forcing evolution to backtrack.

Newer network analysis techniques such as selection-weighted attraction graphing (SWAG) also use a dimensionless genotype space.

For example, a delivery truck with a number of destination addresses can take a large variety of different routes, but only very few will result in a short driving time.

In order to use many common forms of evolutionary optimization, one has to define for every possible solution s to the problem of interest (i.e., every possible route in the case of the delivery truck) how 'good' it is.

However, in some cases (for example, preference-based interactive evolutionary computation) the relevance is more limited, because there is no guarantee that human preferences are consistent with a single fitness assignment.

The two concepts only differ in that physicists traditionally think in terms of minimizing the potential function, while biologists prefer the notion that fitness is being maximized.

Since the human mind struggles to think in greater than three dimensions, 3D topologies can mislead when discussing highly multi-dimensional fitness landscapes.

It is fundamentally possible to measure (even if not to visualise) some of the parameters of landscape ruggedness and of peak number, height, separation, and clustering.