A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
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
Metamodel-Assisted Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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
Evolutionary Computation at the Edge of Feasibility
PPSN IV Proceedings of the 4th International Conference on 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
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A mutation operator for evolution strategies to handle constrained problems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On three new approaches to handle constraints within evolution strategies
Natural Computing: an international journal
Sex and death: towards biologically inspired heuristics for constraint handling
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
On the Behaviour of the (1+1)-ES for a Simple Constrained Problem
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Premature Convergence in Constrained Continuous Search Spaces
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Constraint-Handling in Evolutionary Optimization
Constraint-Handling in Evolutionary Optimization
Surrogate constraint functions for CMA evolution strategies
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Local meta-models for optimization using evolution strategies
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
ICHEA: a constraint guided search for improving evolutionary algorithms
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
An incremental approach to solving dynamic constraint satisfaction problems
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
ICHEA for discrete constraint satisfaction problems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
A survey on optimization metaheuristics
Information Sciences: an International Journal
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Evolution strategies are successful global optimization methods. In many practical numerical problems constraints are not explicitly given. Evolution strategies have to incorporate techniques to optimize in restricted solution spaces. Famous constraint-handling techniques are penalty and multiobjective approaches. Past work has shown that in particular an ill-conditioned alignment between the coordinate system of Gaussian mutation and the constraint boundaries leads to premature convergence. Covariance matrix adaptation evolution strategies offer a solution to this alignment problem. Last, metamodeling of the constraint boundary leads to significant savings of constraint function calls and to a speedup by repairing infeasible solutions. This work gives a brief overview over constraint-handling methods for evolution strategies by demonstrating the approaches experimentally on two exemplary constrained problems.