A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A Hybrid Evolutionary Approach for Multicriteria Optimization Problems: Application to the Flow Shop
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper proposes a probabilistic local search based memetic algorithm for the task scheduling problem with the objective to minimize the maximum completion time, which is known to be NP-Hard problem. It has been proven to be NP-Complete for which optimal solutions can be found only after an exhaustive search. The main positive effect of the proposed approach is by choosing only good individuals as initial solutions for Local search thereby assigning an appropriate local search direction to each initial solution. The proposed probabilistic approach is compared with the non probabilistic memetic approach where tabu search act as local search. From these observations, it is found that the minimum local search probability will avoid the premature convergence of MA and also reduce the processing time rather than trapping into a local minima.