Global optimization
The nature of statistical learning theory
The nature of statistical learning theory
A new adaptive penalty scheme for genetic algorithms
Information Sciences: an International Journal - Special issue: Evolutionary computation
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Feasibility structure modeling: an effective chaperone for constrained memetic algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
A simple ranking and selection for constrained evolutionary optimization
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
QuickVina: Accelerating AutoDock Vina Using Gradient-Based Heuristics for Global Optimization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computers & Mathematics with Applications
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Regressions has successfully been incorporated into memetic algorithm (MA) to build surrogate models for the objective or constraint landscape of optimization problems. This helps to alleviate the needs for expensive fitness function evaluations by performing local refinements on the approximated landscape. Classifications can alternatively be used to assist MA on the choice of individuals that would experience refinements. Support-vector-assisted MA were recently proposed to alleviate needs for function evaluations in the inequality-constrained optimization problems by distinguishing regions of feasible solutions from those of the infeasible ones based on some past solutions such that search efforts can be focussed on some potential regions only. For problems having equality constraints, however, the feasible space would obviously be extremely small. It is thus extremely difficult for the global search component of the MA to produce feasible solutions. Hence, the classification of feasible and infeasible space would become ineffective. In this paper, a novel strategy to overcome such limitation is proposed, particularly for problems having one and only one equality constraint. The raw constraint value of an individual, instead of its feasibility class, is utilized in this work.