Genetic & Evolutionary Biometrics
Aniesha Alford, Joseph Shelton, Joshua Adams, Derrick Leflore, Michael Payne, Jonathan E. Turner, Vincent McLean, Robert Benson, Gerry Vernon Dozier, Kelvin S. Bryant, John Charles Kelly · InTech eBooks · 2012
Genetic & Evolutionary Computation (GEC) is the field of study devoted to the design, development, and analysis of problem solvers based on natural selection [1-4] and has been successfully applied to a wide range of complex, real world optimization problems in the areas of robotics [5], scheduling [6], music generation [7], aircraft design [1], and cyber secur‐ ity [8-11], just to name a few. Genetic and Evolutionary Computations (referred to as GECs) differ from most traditional problems solvers in that they are stochastic methods that evolve a population of candidate solutions (CSs) rather than just operating on a single CS. Due to the evolutionary nature of GECs, they are able to discover a wide variety of novel solutions to a particular problem at hand – solutions that radically differ from those developed by tradition‐ al problem solvers [3,12,13].