Hybridizing MOEAs with Mathematical-Programming Techniques

[1]  S. Hua,et al.  A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. , 2001, Journal of molecular biology.

[2]  Jatinder N. D. Gupta,et al.  Neural networks in business: techniques and applications for the operations researcher , 2000, Comput. Oper. Res..

[3]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[4]  J. Dennis,et al.  A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems , 1997 .

[5]  Dara Curran,et al.  An Evolutionary Neural Network Approach to Intrinsic Plagiarism Detection , 2009, AICS.

[6]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[7]  Nicola Beume,et al.  SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..

[8]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[9]  Oliver Kramer,et al.  On the hybridization of SMS-EMOA and local search for continuous multiobjective optimization , 2009, GECCO '09.

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Saúl Zapotecas Martínez,et al.  A Proposal to Hybridize Multi-Objective Evolutionary Algorithms with Non-gradient Mathematical Programming Techniques , 2008, PPSN.

[12]  Thierry Denoeux,et al.  Neural networks for process control and optimization: two industrial applications. , 2003, ISA transactions.

[13]  J. Hammersley MONTE CARLO METHODS FOR SOLVING MULTIVARIABLE PROBLEMS , 1960 .

[14]  Kaisa Miettinen,et al.  Some Methods for Nonlinear Multi-objective Optimization , 2001, EMO.

[15]  C. A. Coello Coello,et al.  Hybridizing evolutionary strategies with continuation methods for solving multi-objective problems , 2008 .

[16]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[17]  Ah Chung Tsoi,et al.  Universal Approximation Using Feedforward Neural Networks: A Survey of Some Existing Methods, and Some New Results , 1998, Neural Networks.

[18]  Andrew R. Webb,et al.  Statistical Pattern Recognition: Webb/Statistical Pattern Recognition , 2011 .

[19]  Balram Suman,et al.  Study of simulated annealing based algorithms for multiobjective optimization of a constrained problem , 2004, Comput. Chem. Eng..

[20]  Wenhui Fan,et al.  Hybrid Non-dominated Sorting Differential Evolutionary Algorithm with Nelder-Mead , 2010, 2010 Second WRI Global Congress on Intelligent Systems.

[21]  J. Halton On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .

[22]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[23]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[24]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[25]  Yulei Jiang,et al.  A multitarget training method for artificial neural network with application to computer-aided diagnosis. , 2012, Medical physics.

[26]  Dennis Gabor,et al.  Communication Theory and Cybernetics , 1954 .

[27]  Michel Happiette,et al.  A neural clustering and classification system for sales forecasting of new apparel items , 2007, Appl. Soft Comput..

[28]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[29]  Xiaolin Hu,et al.  Hybridization of the multi-objective evolutionary algorithms and the gradient-based algorithms , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[30]  Eugenius Kaszkurewicz,et al.  Existence and stability of a unique equilibrium in continuous-valued discrete-time asynchronous Hopfield neural networks , 1996, IEEE Trans. Neural Networks.

[31]  Peter Sturmey,et al.  On Some Recent Claims for the Efficacy of Cognitive Therapy for People with Intellectual Disabilities , 2006 .

[32]  Sanjeev R. Kulkarni,et al.  An Elementary Introduction to Statistical Learning Theory: Kulkarni/Statistical Learning Theory , 2011 .

[33]  Amir F. Atiya,et al.  Introduction to the special issue on neural networks in financial engineering , 2001, IEEE Trans. Neural Networks.

[34]  Eugenius Kaszkurewicz,et al.  Steepest descent with momentum for quadratic functions is a version of the conjugate gradient method , 2004, Neural Networks.

[35]  Stephen Schecter,et al.  Structure of the first-order solution set for a class of nonlinear programs with parameters , 1986, Math. Program..

[36]  S. Dreyfus The numerical solution of variational problems , 1962 .

[37]  Kalyanmoy Deb,et al.  A Local Search Based Evolutionary Multi-objective Optimization Approach for Fast and Accurate Convergence , 2008, PPSN.

[38]  R. Storn,et al.  Differential evolution a simple and efficient adaptive scheme for global optimization over continu , 1997 .

[39]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[40]  Peter A. N. Bosman,et al.  Exploiting gradient information in numerical multi--objective evolutionary optimization , 2005, GECCO '05.

[41]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[42]  J. Nilsson,et al.  Artificial neural networks predict survival from pancreatic cancer after radical surgery. , 2013, American journal of surgery.

[43]  Jörg Fliege,et al.  Newton's Method for Multiobjective Optimization , 2009, SIAM J. Optim..

[44]  J. Goodman,et al.  Neural networks for computation: number representations and programming complexity. , 1986, Applied optics.

[45]  S. Schäffler,et al.  Stochastic Method for the Solution of Unconstrained Vector Optimization Problems , 2002 .

[46]  J. Mendel Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[47]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[48]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[49]  Andrzej Jaszkiewicz,et al.  Do multiple-objective metaheuristics deliver on their promises? A computational experiment on the set-covering problem , 2003, IEEE Trans. Evol. Comput..

[50]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[51]  John E. Dennis,et al.  Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems , 1998, SIAM J. Optim..

[52]  S. Grossberg,et al.  Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.

[53]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[54]  Carlos A. Coello Coello,et al.  HCS: A New Local Search Strategy for Memetic Multiobjective Evolutionary Algorithms , 2010, IEEE Transactions on Evolutionary Computation.

[55]  Feng Cheng,et al.  Applications of Artificial Neural Network Modeling in Drug Discovery , 2012 .

[56]  Joshua D. Knowles Local-search and hybrid evolutionary algorithms for Pareto optimization , 2002 .

[57]  Carlos A. Coello Coello,et al.  On Gradient-Based Local Search to Hybridize Multi-objective Evolutionary Algorithms , 2013, EVOLVE.

[58]  Martin Brown,et al.  Effective Use of Directional Information in Multi-objective Evolutionary Computation , 2003, GECCO.

[59]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[60]  Kurt Miller,et al.  Artificial neural networks and prostate cancer—tools for diagnosis and management , 2013, Nature Reviews Urology.

[61]  Sanjoy Das,et al.  Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models , 2004, GECCO.

[62]  L. Lasdon,et al.  On a bicriterion formation of the problems of integrated system identification and system optimization , 1971 .

[63]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[64]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[65]  Thomas M. Cover,et al.  Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..

[66]  Jörg Fliege,et al.  Steepest descent methods for multicriteria optimization , 2000, Math. Methods Oper. Res..

[67]  Bernard F. Buxton,et al.  Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data Analysis , 2001, Comput. Chem..

[68]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[69]  E. Kaszkurewicz,et al.  On a class of globally stable neural circuits , 1994 .

[70]  Kalyanmoy Deb,et al.  A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design , 2001, EMO.

[71]  Jooyoung Park,et al.  Approximation and Radial-Basis-Function Networks , 1993, Neural Computation.

[72]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[73]  Andrea Roli,et al.  A neural network approach for credit risk evaluation , 2008 .