The importance of using heuristics in an optimization algorithm is well established, particularly in solving complex real-world problems. It is then expected that users know certain key problem information a priori and are able to implement the information in a suitable optimization algorithm. However, in many problems, such problem information may not be available before an optimization task is performed, thereby making the heuristics-based algorithms difficult to be implemented. In this paper, we suggest a `derived heuristics' based optimization methodology for this purpose. In such a method, past results from an optimization algorithm are utilized to derive problem heuristics and then used in a future applications to achieve a faster and more accurate optimization task. Heuristics can also be derived from the optimization run and used in subsequent iterations. In a particular gold processing plant optimization problem, we demonstrate the use of derived heuristics by developing a customized evolutionary optimization procedure which is capable of handling various complexities offered by the problem. The proposed derived heuristics based evolutionary optimization (DHEO) is generic and the results of this paper is motivating for evolutionary computation researchers to apply the methodology to other more complex real-world problems.
