Fitness landscape

In evolutionary biology, fitness landscapes or adaptive landscapes (types of evolutionary landscapes) are used to visualize the relationship between genotypes and reproductive success. It is assumed that every genotype has a well-defined replication rate (often referred to as fitness). This fitness is the 'height' of the landscape. Genotypes which are similar are said to be 'close' to each other, while those that are very different are 'far' from each other. The set of all possible genotypes, their degree of similarity, and their related fitness values is then called a 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. In evolutionary biology, fitness landscapes or adaptive landscapes (types of evolutionary landscapes) are used to visualize the relationship between genotypes and reproductive success. It is assumed that every genotype has a well-defined replication rate (often referred to as fitness). This fitness is the 'height' of the landscape. Genotypes which are similar are said to be 'close' to each other, while those that are very different are 'far' from each other. The set of all possible genotypes, their degree of similarity, and their related fitness values is then called a 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. In evolutionary optimization problems, fitness landscapes are evaluations of a fitness function for all candidate solutions (see below). In all fitness landscapes, height represents and is a visual metaphor for fitness. There are three distinct ways of characterizing the other dimensions, though in each case distance represents and is a metaphor for degree of dissimilarity. Fitness landscapes are often conceived of as ranges of mountains. There exist local peaks (points from which all paths are downhill, i.e. to lower fitness) and valleys (regions from which many paths lead uphill). A fitness landscape with many local peaks surrounded by deep valleys is called rugged. If all genotypes have the same replication rate, on the other hand, a fitness landscape is said to be flat. An evolving population typically climbs uphill in the fitness landscape, by a series of small genetic changes, until a local optimum is reached. Wright visualized a genotype space as a hypercube. No continuous genotype 'dimension' is defined. Instead, a network of genotypes are connected via mutational paths. Stuart Kauffman's NK model falls into this category of fitness landscape. Newer network analysis techniques such as selection-weighted attraction graphing (SWAG) also use a dimensionless genotype space. Wright's mathematical work described fitness as a function of allele frequencies. Here, each dimension describes an allele frequency at a different gene, and goes between 0 and 1. In the third kind of fitness landscape, each dimension represents a different phenotypic trait. Under the assumptions of quantitative genetics, these phenotypic dimensions can be mapped onto genotypes. See the visualizations below for examples of phenotype to fitness landscapes.