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Concept
Fitness landscape
Summary
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 – in the infinite time limit – a local optimum is reached.
Note that a local optimum cannot always be found even in evolutionary time: if the local optimum can be found in a reasonable amount of time then the fitness landscape is called "easy" and if the time required is exponential then the fitness landscape is called "hard".
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Fitness function
A fitness function is a particular type of objective function that is used to summarise, as a single figure of merit, how close a given design solution is to achieving the set aims. Fitness functions are used in evolutionary algorithms (EA), such as genetic programming and genetic algorithms to guide simulations towards optimal design solutions. In the field of EAs, each design solution is commonly represented as a string of numbers (referred to as a chromosome).
Universal Darwinism
Universal Darwinism, also known as generalized Darwinism, universal selection theory, or Darwinian metaphysics, is a variety of approaches that extend the theory of Darwinism beyond its original domain of biological evolution on Earth. Universal Darwinism aims to formulate a generalized version of the mechanisms of variation, selection and heredity proposed by Charles Darwin, so that they can apply to explain evolution in a wide variety of other domains, including psychology, linguistics, economics, culture, medicine, computer science, and physics.
Evolvability
Evolvability is defined as the capacity of a system for adaptive evolution. Evolvability is the ability of a population of organisms to not merely generate genetic diversity, but to generate adaptive genetic diversity, and thereby evolve through natural selection. In order for a biological organism to evolve by natural selection, there must be a certain minimum probability that new, heritable variants are beneficial. Random mutations, unless they occur in DNA sequences with no function, are expected to be mostly detrimental.