Source : Free On-Line Dictionary of Computing

evolutionary algorithm
     
        (EA) An {algorithm} which incorporates aspects of natural
        selection or survival of the fittest.  An evolutionary
        algorithm maintains a population of structures (usually
        randomly generated initially), that evolves according to rules
        of selection, recombination, mutation and survival, referred
        to as genetic operators.  A shared "environment" determines
        the fitness or performance of each individual in the
        population.  The fittest individuals are more likely to be
        selected for reproduction (retention or duplication), while
        recombination and mutation modify those individuals, yielding
        potentially superior ones.
     
        EAs are one kind of {evolutionary computation} and differ from
        {genetic algorithm}s.  A GA generates each individual from
        some encoded form known as a "chromosome" and it is these
        which are combined or mutated to breed new individuals.
     
        EAs are useful for optimisation when other techniques such as
        {gradient descent} or direct, analytical discovery are not
        possible.  Combinatoric and real-valued function optimisation
        in which the optimisation surface or fitness landscape is
        "rugged", possessing many {locally optimal} solutions, are
        well suited for evolutionary algorithms.
     
        (1995-02-03)