|Project Description: ||The general nonlinear programming (NLP) problem requires finding the optimum point (minimum or maximum) of a function of n real variables subjected to some given constraints. There are numerous situations in science and engineering where the optimum is bounded adding complexity to the optimization problem. Solving the general NLP problem analytically is possible only in a limited number of cases, so numerical methods are therefore used. Of these methods, evolutionary algorithms are very promising, because of their robustness, ability to deal with multi-modal and noisy functions and being very well suited to parallelization. Solving constrained nonlinear programming problems using evolutionary computations is considered. An approach for this is quoted in the literature [Simionescu 2004]. According to the method two populations are evolved, one population (females) is evolved inside the feasible domain of the design space and a second population (males) is evolved outside this feasible domain. Both populations can be independently subject to crossover and mutation operations and the design space explored. Female-male crossover however ensures the desirable increase in the search pressure upon the boundaries of the feasible space - it is known that in many optimization problems the global optimum is bounded. This will be explored using some test functions. A parallelized version of this algorithm is implemented part of this project, and its effectiveness is tested using benchmark problems taken from literature.