Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Genetic Optimization Using A Penalty Function
Proceedings of the 5th International Conference on Genetic Algorithms
Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
When Selection Meets Seduction
Proceedings of the 6th International Conference on Genetic Algorithms
A Segregated Genetic Algorithm for Constrained Structural Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Evolving Objects: A General Purpose Evolutionary Computation Library
Selected Papers from the 5th European Conference on Artificial Evolution
A penalty-based evolutionary algorithm for constrained optimization
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
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Adaptivity has become a key issue in Evolutionary Algorithms, since early works in Evolution Strategies. The idea of letting the algorithm adjust its own parameters for free is indeed appealing. This paper proposes to use adaptive mechanisms at the population level for constrained optimization problems in three important steps of the evolutionary algorithm: First, an adaptive penalty function takes care of the penalty coefficients according to the proportion of feasible individuals in the current population; Second, a Seduction/Selection strategy is used to mate feasible individuals with infeasible ones and thus explore the region around the boundary of the feasible domain; Last, selection is tuned to favor a given number of feasible individuals. A detailed discussion of the behavior of the algorithm on two small constrained problems enlights adaptivity at work. Finally, experimental results on eleven test cases from the literature demonstrate the power of this approach.