Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
How to solve it: modern heuristics
How to solve it: modern heuristics
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
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
A Segregated Genetic Algorithm for Constrained Structural Optimization
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 Multi-objective Approach to Constrained Optimisation of Gas Supply Networks: the COMOGA Method
Selected Papers from AISB Workshop on Evolutionary Computing
On three new approaches to handle constraints within evolution strategies
Natural Computing: an international journal
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
A review of constraint-handling techniques for evolution strategies
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
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Constrained continuous optimization is still an interesting field of research. Many heuristics have been proposed in the last decade. Most of them are based on penalty functions. Here, we experimentally investigate the two constraint handling heuristics proposed by Kramer and Schwefel. The two sexes evolution strategy (TSES) is inspired by the biological concept of sexual selection and pairing. The death penalty step control evolution strategy (DSES) is based on the controlled reduction of a minimum step size depending on the distance to the infeasible search space. These two methods are able to overcome the problem of premature mutation strength reduction, a result of the self-adaptation mechanism of evolution strategies in constrained environments. All methods are experimentally evaluated on a couple of typical constrained test problems. These experiments offer recommendations for the TSES population ratios and the speed of the ε-reduction process of the DSES.