Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Shall We Repair? Genetic AlgorithmsCombinatorial Optimizationand Feasibility Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
Optimal coordination of over-current relays using modified differential evolution algorithms
Engineering Applications of Artificial Intelligence
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Society and civilization: An optimization algorithm based on the simulation of social behavior
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A hybrid cuckoo search and genetic algorithm for reliability-redundancy allocation problems
Computers and Industrial Engineering
Solving system-level synthesis problem by a multi-objective estimation of distribution algorithm
Expert Systems with Applications: An International Journal
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In this paper, a hybrid genetic algorithm with flexible allowance technique (GAFAT) is proposed for solving constrained engineering design optimization problems by fusing center based differential crossover (CBDX), Levenberg-Marquardt mutation (LMM) and non-uniform mutation (NUM). Inheriting the merits of mutation of differential evolution (DE), the proposed CBDX is a multi-parent recombination operator for generating offspring based on a parent vector and two parent center vectors. As an improvement of the gradient-based mutation, the proposed LMM is more numerically stable when enhancing the feasibility of the new individuals. To enrich the population diversity, NUM is incorporated into the hybrid algorithm. In addition, a flexible allowance technique (FAT) is designed and used in the hybrid algorithm to balance the selection of bad feasible solutions and good infeasible solutions. The proposed GAFAT is first tested based on the 13 widely used benchmark functions, which shows that GAFAT is of better or competitive performances when compared with six existing algorithms. The, GAFAT is applied to solve six well-known constrained engineering design problems, which also shows that GAFAT is of superior searching quality with fewer evaluation times than other algorithms. Finally, GAFAT is successfully applied to solve a real pipe frequency improvement problem.