Improving Evolutionary Algorithm Design for Complex Real Time Problems

Several types of evolutionary algorithms (EAs) have been applied to solve the project scheduling problem (PSP).  The  performance of these EAs highly depends on design choices for the EA. Based on the dedications of particular tasks the employee can work on multiple jobs simultaneously. This consist of normalizing employees’ dedication for different tasks to ensure they are not working overtime; a fitness function that requires fewer pre-defined parameters and provides a clear gradient towards feasible solutions; and an improved representation and mutation operator. Both the theoretical and empirical findings show that the  design is very effective. A repair mechanism is  that which  facilitates the search for feasible schedules without overwork. Their repair mechanism considers the maximum total dedication of any employee at any point of time during the generated schedule. the problem of overwork can be alleviated and hence  can remove a crucial obstacle in the search process of EAs by using the following an approach : normalisation. Combining the use of normalization to a population gave the best results in the experiments, and normalization was a principle insight for the practical effectiveness of the existing system. Existing system concludes that normalisation is not always effective. The proposed work is based on comparison of an earlier technique used in this area called ‘repair mechanism’. Proposing the collaboration of both techniques to arrive at the best optimal solutions for the PSP and at the end testing the feasibility of the proposed idea.