A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies
Proceedings of the 8th annual conference on Genetic and evolutionary computation
On three new approaches to handle constraints within evolution strategies
Natural Computing: an international journal
Evolutionary algorithms for constrained parameter optimization problems
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
Adaptive Encoding: How to Render Search Coordinate System Invariant
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Surrogate constraint functions for CMA evolution strategies
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Active covariance matrix adaptation for the (1+1)-CMA-ES
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Towards non-linear constraint estimation for expensive optimization
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
| Hi-index | 0.00 |
This paper introduces a novel constraint handling approach for covariance matrix adaptation evolution strategies (CMA-ES). The key idea is to approximate the directions of the local normal vectors of the constraint boundaries by accumulating steps that violate the respective constraints, and to then reduce variances of the mutation distribution in those directions. The resulting strategy is able to approach the boundary of the feasible region without being impeded in its ability to search in directions tangential to the boundaries. The approach is implemented in the (1+1)-CMA-ES and evaluated numerically on several test problems. The results compare very favourably with data for other constraint handling approaches applied to unimodal test problems that can be found in the literature.