Global optimization
An OL(n3) primal interior point algorithm for convex quadratic programming
Mathematical Programming: Series A and B
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Adaptive global optimization with local search
Adaptive global optimization with local search
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
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Advanced fitness landscape analysis and the performance of memetic algorithms
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Simple addition of ranking method for constrained optimization in evolutionary algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
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IEEE Transactions on Evolutionary Computation
A simple ranking and selection for constrained evolutionary optimization
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
Evolutionary algorithms + domain knowledge = real-world evolutionary computation
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Handling undefined vectors in expensive optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
A computational intelligence algorithm for simulation-driven optimization problems
Advances in Engineering Software
A classifier-assisted framework for expensive optimization problems: a knowledge-mining approach
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
A computational intelligence algorithm for expensive engineering optimization problems
Engineering Applications of Artificial Intelligence
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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An important issue in designing memetic algorithms (MAs) is the choice of solutions in the population for local refinements, which becomes particularly crucial when solving computationally expensive problems. With single evaluation of the objective/constraint functions necessitating tremendous computational power and time, it is highly desirable to be able to focus search efforts on the regions where the global optimum is potentially located so as not to waste too many function evaluations. For constrained optimization, the global optimum must either be located at the trough of some feasible basin or some particular point along the feasibility boundary. Presented in this paper is an instance of optinformatics where a new concept of modeling the feasibility structure of inequality-constrained optimization problems--dubbed the feasibility structure modeling-- is proposed to perform geometrical predictions of the locations of candidate solutions in the solution space: deep inside any infeasible region, nearby any feasibility boundary, or deep inside any feasible region. This knowledge may be unknown prior to executing an MA but it can be mined as the search for the global optimum progresses. As more solutions are generated and subsequently stored in the database, the feasibility structure can thus be approximated more accurately. As an integral part, a new paradigm of incorporating the classification--rather than the regression--into the framework of MAs is introduced, allowing the MAs to estimate the feasibility boundary such that effective assessments of whether or not the candidate solutions should experience local refinements can be made. This eventually helps preventing the unnecessary refinements and consequently reducing the number of function evaluations required to reach the global optimum.