<P> Phylogenetic trees composed with a nontrivial number of input sequences are constructed using computational phylogenetics methods . Distance - matrix methods such as neighbor - joining or UPGMA, which calculate genetic distance from multiple sequence alignments, are simplest to implement, but do not invoke an evolutionary model . Many sequence alignment methods such as ClustalW also create trees by using the simpler algorithms (i.e. those based on distance) of tree construction . Maximum parsimony is another simple method of estimating phylogenetic trees, but implies an implicit model of evolution (i.e. parsimony). More advanced methods use the optimality criterion of maximum likelihood, often within a Bayesian Framework, and apply an explicit model of evolution to phylogenetic tree estimation . Identifying the optimal tree using many of these techniques is NP - hard, so heuristic search and optimization methods are used in combination with tree - scoring functions to identify a reasonably good tree that fits the data . </P> <P> Tree - building methods can be assessed on the basis of several criteria: </P> <Ul> <Li> efficiency (how long does it take to compute the answer, how much memory does it need?) </Li> <Li> power (does it make good use of the data, or is information being wasted?) </Li> <Li> consistency (will it converge on the same answer repeatedly, if each time given different data for the same model problem?) </Li> <Li> robustness (does it cope well with violations of the assumptions of the underlying model?) </Li> <Li> falsifiability (does it alert us when it is not good to use, i.e. when assumptions are violated?) </Li> </Ul> <Li> efficiency (how long does it take to compute the answer, how much memory does it need?) </Li>

Definition this is the evolutionary history of a species or group of related species