This paper introduces in details a genetic algorithm-called BASIC, which is designed to take advantage of well known genetic schemes so as to be able to deal with numerous optimization problems. BASIC GA follows all common steps of the genetic algorithms. It involves real representation schemes for both real and integer variables. Three biased selection schemes for reproduction; four for recombination and three for mutation are applied in it and a new selection scheme for replacement is approached. BASIC GA can be easy adjusted to the concrete problems by fitting its global and local parameters. It provides an opportunity to the genetic operators to be extended with new schemes. A range of various optimization problems has been solved to test its capability. To handle all sorts of constraints the static and dynamic penalty functions are used. The solutions obtained are commensurable with other genetic algorithms and solution techniques. (c) 2006 Elsevier Ltd. All rights reserved.