Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic AlgorithmsNumerical Optimizationand Constraints
Proceedings of the 6th International Conference on Genetic Algorithms
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary programming techniques for constrained optimizationproblems
IEEE Transactions on Evolutionary Computation
Coevolutionary augmented Lagrangian methods for constrainedoptimization
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
A hybrid quantum evolutionary algorithm for solving engineering optimization problems
International Journal of Hybrid Intelligent Systems
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Evolutionary Algorithms are amongst the best known methods of solving difficult constraint optimization problems, for which traditional methods are not applicable. However, there are no inbuilt or organic mechanisms available in Evolutionary Algorithms for handling constraints in optimization problems. These problems are solved by converting or treating them as unconstrained optimization problems. Several constraint handling techniques have been developed and reported in literature, of which, the penalty factor and feasibility rules are the most promising and widely used for such purposes. However, each of these techniques has its own advantages and disadvantages and often require fine tuning of one or more parameters, which in itself becomes an optimization problem. This paper presents a hybrid constraint handling technique for a two population adaptive coevolutionary algorithm, which uses a self determining and regulating penalty factor method as well as feasibility rules for handling constraints. Thus, the method overcomes the drawbacks in both the methods and utilizes their strengths to effectively and efficiently handle constraints. The simulation on ten benchmark problems demonstrates the efficacy of the approach.