Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Theoretical aspects of evolutionary computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Operations Research
Constraint Handling in Genetic Algorithms: The Set Partitioning Problem
Journal of Heuristics
Some Guidelines for Genetic Algorithms with Penalty Functions
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic AlgorithmsNumerical Optimizationand Constraints
Proceedings of the 6th International Conference on Genetic Algorithms
Co-evolutionary Constraint Satisfaction
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A new adaptive penalty scheme for genetic algorithms
Information Sciences: an International Journal - Special issue: Evolutionary computation
The second generation of self-organizing adaptive penalty strategy for constrained genetic search
Advances in Engineering Software
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
Genetic algorithms in constrained optimization
Mathematical and Computer Modelling: An International Journal
BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems
Computers and Operations Research
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Constraint handling is one of the major concerns when applying genetic algorithms (GAs) to solve constrained optimization problems. This paper proposes a boundary simulation method to address inequality constraints for GAs. This method can efficiently generate a feasible region boundary point set to approximately simulate the boundary of the feasible region. Based on the results of the boundary simulation method, GAs can start the genetic search from the boundary of the feasible region or the feasible region itself directly. Furthermore, a series of genetic operators that abandon or repair infeasible individuals produced during the search process is also proposed. The numerical experiments indicate that the proposed method can provide competitive results compared with other studies.