Despite the existence of a number of procedures for real-parameter optimization using evolutionary algorithms, there is still a need of a systematic and unbiased comparison of different approaches on a carefully chosen set of test problems. In this paper, we develop a steady-state, population-based optimization algorithm which allows the main search principles to be independently designed. The algorithm so developed is applied to a set of 25 test problems and results on 10 and 30 dimensions are presented. Although the proposed procedure cannot find the exact optimum within the specified number of function evaluations, in most problems, the algorithm shows steady progress towards the optimum. Moreover, it is also observed that the performance of the algorithm does not get affected by the rotation of the functions, discontinuity and embedded noise in function description.