Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Evolutionary Computation at the Edge of Feasibility
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
A Novel Component-Based Model and Ranking Strategy in Constrained Evolutionary Optimization
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
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Finding a solution to constrained optimization problems (COPs) with differential evolution (DE) is a promising research issue. This paper proposes a novel algorithm to improve the original mutation and selection operators of DE. It explored some benefits from the component model and self-adaption mechanism, while solving the constrained optimization problems. Six benchmark functions about constraint problems are used in the experiment to evaluate the performance of the proposed algorithm. The experiment results demonstrate its effectiveness compared with other the current state-of-the art approaches in constraint optimization such as KM, SAFF and ISR.