Abstract
Soft Computing [1–3], as the name suggests, deals with the soft meaning of concepts. This is a relatively new computing paradigm which entails a synergistic integration of essentially four other computing paradigms, viz., neural networks, fuzzy logic, rough sets and evolutionary computation, incorporating probabilistic reasoning (belief networks, genetic algorithms and chaotic systems). These computing paradigms are conjoined to provide a framework for flexible information processing applications designed to operate in the real-world. Bezdek [4] referred to this synergism as computational intelligence. According to Prof. Zadeh, soft computing is “an emerging approach to computing, which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision” [5].
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References
L.A. Zadeh, Fuzzy logic, neural networks, and soft computing. Commun. ACM 37, 77–84 (1994)
N.R. Pal, Soft computing for pattern recognition. Fuzzy Sets Syst. 103, 197–200 (1999)
J.C. Bezdek, On the relationship between neural networks, pattern recognition and intelligence. Int. J. Approx. Reason. 6, 85–107 (1992)
J.R. Jang, C. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice-Hall, Englewood Cliffs, 1997)
S. Bhattacharyya, U. Maulik, S. Bandyopadhyay, Soft computing and its applications, in Kansei Engineering and Soft Computing: Theory and Practice ed. by Y. Dai, B. Chakraborty, M. Shi (IGI Global, Hershey, 2011), pp. 1–30
L.A. Zadeh, Fuzzy sets. Inf. Control 8, 338–353 (1965)
Z. Pawlak, Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)
D.E. Goldberg, Genetic Algorithms: Search, Optimization and Machine Learning (Addison-Wesley, New York, 1989)
L. Davis (ed.), Handbook of Genetic Algorithms (Van Nostrand Reinhold, New York, 1991)
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs (Springer, New York, 1992)
S. Bandyopadhyay, S.K. Pal, Classification and Learning Using Genetic Algorithms: Application in Bioinformatics and Web Intelligence (Springer-Verlag, Hiedelberg, Germany, 2007)
M. Dorigo, Optimization, Learning and Natural Algorithms. Ph.D. thesis, Politecnico di Milano, Italy, 1992
J. Kennedy, R. Eberhart, Particle Swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, Perth, vol. 4, pp. 1942–1948, 1995
J. Kennedy, R. Eberhart, Swarm Intelligence (Morgan Kaufmann, San Francisco, CA, USA, 2001)
R. Storn, K. Price, Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)
S. Kirkpatrik, C. Gelatt, M. Vecchi, Optimization by simulated annealing. Science 22, 671–680 (1983)
T. Murata, H. Ishibuchi, MOGA: multi-objective genetic algorithms, in Proceedings of the 1995 IEEE International Conference on Evolutionary Computation, Perth, 29 Nov–1 Dec 1995
U. Maulik, S. Bandyopadhyay, A. Mukhopadhyay, Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics (Springer, Berlin/Heidelberg, 2011)
F. Xue, A.C. Sanderson, R.J. Graves, Pareto-based multi-objective differential evolution, in Proceedings of the 2003 Congress on Evolutionary Computation (CEC’2003), Canberra, vol. 2 (IEEE, 2003), pp. 862–869
F. Xue, Multi-objective Differential Evolution: Theory and Applications. Ph.D. thesis, Rensselaer Polytechnic Institute, Troy, New York, 2004
K. Smith, R. Everson, J. Fieldsend, Dominance measures for multi-objective simulated annealing, in Proceedings of 2004 IEEE Congress on Evolutionary Computation (CEC’2004), Portland, Oregon, USA, vol. 1, June 2004, ed. by E. Zitzler, K. Deb, L. Thiele, C.A. Coello Coello, D. Corne (IEEE Service Center, 2004), pp. 23–30
W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)
S. Kumar, Neural Networks: A Classroom Approach (Tata McGraw-Hill, New Delhi, 2004)
S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd edn. (Prentice Hall, Upper Saddle River, 1999)
R. Rojas, Neural Networks: A Systematic Introduction (Springer, Berlin, 1996)
T. Kohonen, Self-Organization and Associative Memory (Springer, London, 1984)
T. Kohonen, Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)
T.J. Ross, Fuzzy Logic with Engineering Applications (McGraw-Hill, Hightstown, 1995)
S. Bhattacharyya, U. Maulik, P. Dutta, High-speed target tracking by fuzzy hostility-induced segmentation of optical flow field. Int. J. Appl. Soft Comput. 9, 126–134 (2009)
S. Bhattacharyya, P. Dutta, Multiscale object extraction with MUSIG and MUBET with CONSENT: a comparative study, in Proceedings of KBCS 2004, Hyderabad, India, Dec 2004, pp. 100–109
S. Bandyopadhyay, S. Saha, U. Maulik, K. Deb, A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans. Evol. Comput. 12, 269–283 (2008)
S. Bhattacharyya, Neural networks: evolution, topologies, learning algorithms and applications, in Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, ed. by V. Mago, N. Bhatia (IGI Global, Hershey, 2012)
G.G. Matthews, Cellular Physiology of Nerve and Muscle (Blackwell Scientific Publications, Boston, 1991)
A. Brown, Nerve Cells and Nervous Systems (Springer, Berlin, 1991)
F. Li, J.Z. Tsien, Clinical implications of basic research: memory and the NMDA receptors. N. Engl. J. Med. 361, 302 (2009)
R. Dingledine, K. Borges, D. Bowie, S.F. Traynelis, The glutamate receptor ion channels. Pharmacol. Rev. 51(1), 7–61 (1999)
O. Steward, Principles of Cellular, Molecular, and Developmental Neuroscience (Springer, New York, 1989)
H. Reichert, Neurobiologie (Georg Thieme, Stuttgart, 1990)
R. Thompson, Das Gehirn: Von der Nervenzelle zur Verhaltenssteuerung (Spektrum der Wissenschaft, Heidelberg, 1990)
T.V.P. Bliss, T. Lomo, Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 331, 331–356 (1973)
T. Kohonen, Self-Organizing Maps. Springer Series in Information Sciences, vol. 30 (Springer, Berlin, Heidelberg, New York, 2001)
B.W. Wah, M.B. Lowrie, G. Li, Computers for symbolic processing, Invited paper. Proc. IEEE 77(4), 509–540 (1989)
W. Maass, C.M. Bishop (eds.), Pulsed Neural Networks (MIT, Cambridge, 1999)
C.T. Leondes, Image Processing and Pattern Recognition. (Neural Network Techniques and Applications), vol. 5 (Academic Press, San Diego, 1998)
J.T. Tou, R.C. Gonzalez, Pattern Recognition Principles (Addison-Wesley, 1974)
R.O. Duda, P.E. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973)
C. Cortes, V.N. Vapnik, Support vector networks. Mach. Learn. 20, 273–297 (1995)
C.J.C. Burges, A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167 (1998)
D.S. Broomhead, D. Lowe, Multivariate functional interpolation and adaptive networks. Complex Syst. 2, 321–355 (1988)
J.A. Anderson, Introduction to Neural Networks (MIT, Cambridge, 1995)
K. Hornik, Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251–257 (1991)
J.J. Hopfield, Neurons with graded response have collective computational properties like those of two state neurons. Proc. Natl. Acad. Sci. U.S.A. 81(10), 3088–3092 (1984)
J.A. Anderson, The BSB model: a simple nonlinear autoassociative neural network, in Associative Neural Memories, ed. by M. Hassoun (Oxford University Press Inc., New York, 1993)
S. Hui, S.H. Zak, Dynamical analysis of the Brain-State-in-a-Box (BSB) neural model. IEEE Trans. Neural Netw. 3, 86–94 (1992)
D.H. Ackley, G.E. Hinton, T.J. Sejnowski, A learning algorithm for Boltzmann machines. Cogn. Sci. 9, 147–169 (1985)
G.E. Hinton, T.J. Sejnowski, Parallel distributed processing: explorations in the microstructure of cognition (learning and relearning in boltzmann machines), in Foundations, ed. by D.E. Rumelhart, J.L. McClelland, the PDP Research Group, vol. 1 (MIT, Cambridge, 1986), pp. 282–317
B. Kosko, Bidirectional associative memories. IEEE Trans. Syst. Man Cybern. 18(1), 49–60 (1988)
B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence (Prentice-Hall, Englewood Cliffs, 1992)
G.A. Carpenter, W.D. Ross, ART-EMAP: a neural network architecture for object recognition by evidence accumulation. IEEE Trans. Neural Netw. 6(4), 805–818 (1995)
L.B. Almeida, F. Silva, Speeding-up backpropagation by data orthonormalization. Artif. Neural Netw. 2, 56–149 (1991)
L.B. Almeida, F. Silva, Speeding-up backpropagation, in Advanced Neural Computers, ed. by R. Eckmiller (North-Holland, Amsterdam, 1990), pp. 151–156
R.A. Jacobs, Increased rates of convergence through learning rate adaptation. Neural Netw. 1, 295–307 (1988)
D. Mandic, J. Chambers, Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability (Wiley, New York, 2001)
M. Gerke, H. Hoyer, Fuzzy backpropagation training of neural networks, in Computational Intelligence Theory and Applications, ed. by B. Reusch (Springer, Berlin, 1997), pp. 416–427
X.G. Wang, Z. Tang, H. Tamura, M. Ishii, W.D. Sun, An improved backpropagation algorithm to avoid the local minima problem. Neurocomputing 56, 455–460 (2004)
W. Bi, X.G. Wang, Z. Tang, H. Tamura, Avoiding the local minima problem in backpropagation algorithm with modified error function. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E88-A, 3645–3653 (2005)
M.T. Hagan, H.B. Demuth, M.H. Beale, Neural Network Design (PWS-Kent, Boston, 1996)
M. Riedmiller, H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, in Proceedings of IEEE International Conference on Neural Networks, San Francisco, 1993, pp. 586–591
C. Charalambous, Conjugate gradient algorithm for efficient training of artificial neural networks. Proc. IEEE 139(3), 301–310 (1992)
M.T. Hagan, M.B. Menhaj, Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1994)
X. Yu, N.K. Loh, W.C. Miller, A new acceleration technique for the backpropagation algorithm, in Proceedings of IEEE International Conference on Neural Networks, San Diego, California, 1993, vol. III, pp. 1157–1161
R. Salomon, Verbesserung konnektionistischer Lernverfahren. Ph.D. thesis, Die nach der Gradientenmethode arbeiten, Technical University of Berlin, 1992
M. Pfister, R. Rojas, Speeding-up backpropagation – a comparison of orthogonal techniques, in Proceedings of International Joint Conference on Neural Networks, Japan, 1993, pp. 517–523
M. Pfister, Hybrid Learning Algorithms for Neural Networks. Ph.D. thesis, Free University, Berlin, 1995
J. Leonard, M.A. Kramer, Improvement of the backpropagation algorithm for training neural networks. Comput. Chem. Eng. 14(3), 337–341 (1990)
A. Kandel, Fuzzy Mathematical Techniques with Applications (Addison-Wesley, New York, 1986)
N. Kasamov, Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering (MIT, Cambridge, 1996)
B. Kosko, S. Isaka, Fuzzy logic. Sci. Am. 269, 76–81 (1993)
S. Bhattacharyya, Object Extraction in a Soft Computing Framework. Ph.D. thesis, Jadavpur University, India, 2007
S. Bhattacharyya, P. Dutta, Fuzzy logic: concepts, system design and applications to industrial informatics, in Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions, ed. by M.A. Khan, A.Q. Ansari (IGI Global, Hershey, 2012)
S. Bhattacharyya, U. Maulik, P. Dutta, Multilevel image segmentation with adaptive image context based thresholding. Int. J. Appl. Soft Comput. 11, 946–962 (2010)
A. Ghosh, N.R. Pal, S.K. Pal, Self-organization for object extraction using a multilayer neural network and fuzziness measures. IEEE Trans. Fuzzy Syst. 1(1), 54–68 (1993)
A. Deluca, S. Termini, A definition of non-probabilistic entropy in the setting of fuzzy set theory. Inf. Control 20, 301–312 (1972)
G. Frege, Grundlagen der arithmetik, vol. 2 (Verlag von Hermann Pohle, Jena, 1893)
L. Polkowski, Rough Sets, Mathematical Foundations (Physica-Verlag, Heidelberg, 2002)
L. Polkowski, A. Skowron, Rough mereological calculi granules: a rough set approach to computation. Int. J. Comput. Intell. 17, 472–479 (2001)
Z. Pawlak, A. Skowron, Rough membership function, in Advances in the Dempster-Schafer Theory of Evidence, ed. by R.E. Yager, M. Fedrizzi, J. Kacprzyk (Wiley, New York, 1994), pp. 251–271
J.J. Grefenstette, Optimization of control parameters for genetic algorithms. IEEE Trans. Syst. Man Cybern. 16, 122–128 (1986)
H. Ishibuchi, M. Nii, T. Murata, Linguistic rule extraction from neural networks and genetic algorithm based rule selection, in Proceedings of IEEE International Conference on Neural Networks, Houston, 1997, pp. 2390–2395
V. Maniezzo, Genetic evolution of the topology and weight distribution of neural networks. IEEE Trans. Neural Netw. 5, 39–53 (1994)
S. Bandyopadhyay, U. Maulik, An evolutionary technique based on K-means algorithm for optimal clustering in R n. Inf. Sci. 146, 221–237 (2002)
S. Bandyopadhyay, U. Maulik, A. Mukhopadhyay, Multiobjective genetic clustering for pixel classification in remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 45, 1506–1511 (2007)
F.A. Cleveland, S.F. Smith, Using genetic algorithms to schedule flow shop releases, in Proceedings of 3rd International Conference on Genetic Algorithms, Fairfax, ed. by J.D. Schaffer (Morgan Kaufmann, San Mateo, CA, 1989), pp. 160–169
S. Bandyopadhyay, A. Bagchi, U. Maulik, Active site driven ligand design: an evolutionary approach. Bioinform. Comput. Biol. 3(5), 1053–1070 (2005)
S. Bandyopadhyay, U. Maulik, D. Roy, Gene identification: classical and computational intelligence approaches. IEEE Trans. Syst. Man Cybern. C 38(1), 55–68 (2008)
P. Mazumder, E.M. Rudnick, Genetic Algorithms for VLSI Design, Layout & Test Automation (Prentice Hall, Upper Saddle River, NJ, 1998)
A. Kumar, R.M. Pathak, M.C. Gupta, Genetic algorithm based approach for designing computer network topology, in Proceedings of ACM Conference on Computer Science, New York, NY, USA, 1993, pp. 358–365
G. Winter, J. Periaux, M. Galan, P. Cuesta (eds.), Genetic Algorithms in Engineering and Computer Science (Wiley, Chichester, 1995)
H. Sgu, R. Hartley, Fast simulated annealing. Phys. Lett. A 122, 157–162 (1987)
F. Glover, Tabu search Part I. ORSA J. Comput. 1, 190–206 (1989)
J. Kennedy, R. Eberhart, Particle Swarm optimization, in Proceedings of IEEE International Conference Neural Networks, Perth, WA, 1995, pp. 1942–1948
H.P. Schwefel (ed.), Numerical Optimization of Computer Models (Wiley, Chichester, 1981)
J.R. Koza (ed.), Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT, Cambridge, 1992)
S. Bandyopadhyay, U. Maulik, J.T.L. Wang (eds.), Analysis of Biological Data: A Soft Computing Approach (World Scientific, Singapore, 2007)
M. Dorigo, L.M. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)
H. Kargupta, K. Deb, D.E. Goldberg, Ordering genetic algorithms and deception, in Proceedings of Parallel Problem Solving from Nature, ed. by R. Manner, B. Manderick (North-Holland, Amsterdam, 1992), pp. 47–56
N.J. Radcliffe, Genetic set recombination, in Foundations of Genetic Algorithms, ed. by L.D. Whitley, vol. 2 (Morgan Kaufmann, San Mateo, 1993), pp. 203–219
J.J. Grefenstette, R. Gopal, B. Rosmaita, D. Van Gucht, Genetic algorithms for the traveling salesman problem, in Proceedings of 1st International Conference on Genetic Algorithms, ed. by J.J. Grefenstette (Lawrence Erlbaum Associates, Hillsdale, 1985), pp. 160–168
J. Holland, Adaptation in neural artificial systems, Technical report, University of Michigan, Ann Arbor, 1975
D.E. Goldberg, K. Deb, B. Korb, Messy genetic algorithms: motivation, analysis, and first results. Complex Syst. 3, 493–530 (1989)
D.E. Goldberg, K. Deb, B. Korb, Do not worry, be messy, in Proceedings of 4th International Conference on Genetic Algorithms and Their Applications, San Diego, USA, ed. by R.K. Belew, J.B. Booker, 1991, pp. 24–30
D.E. Goldberg, K. Deb, H. Kargupta, G. Harik, Rapid, accurate optimization of difficult problems using fast messy genetic algorithms, in Proceedings of 5th International Conference on Genetic Algorithms, Urbana-Champaign, Ill, USA, ed. by S. Forrest, 1993, pp. 56–64
H. Kargupta, S. Bandyopadhyay, Further experimentations on the scalability of the GEMGA. In: Parallel Problem Solving from Nature – PPSN V, 5th International Conference, Amsterdam, ed. by T. Baeck, A. Eiben, M. Schoenauer, H. Schwefel, 1998. Lecture Notes in Computer Science, vol. 1498, pp. 315–324
H. Kargupta, S. Bandyopadhyay, A perspective on the foundation and evolution of the linkage learning genetic algorithms. J. Comput. Methods Appl. Mech. Eng. Special issue on Genetic Algorithms 186, 266–294 (2000)
L.J. Eshelman, J.D. Schaffer, Real-coded genetic algorithms and interval schemata, in Foundations of Genetic Algorithms, ed. by L. Whitley, vol. 2 (Morgan Kaufmann, San Mateo, 1993), pp. 187–202
J.E. Baker, Adaptive selection methods for genetic algorithms, in Proceedings of 1st International Conference on Genetic Algorithms and Their Applications, Pittsburgh, ed. by J.J. Grefenstette (Lawrence Erlbaum Associates, 1985), pp. 101–111
M. Srinivas, L.M. Patnaik, Adaptive probabilities of crossover and mutation in genetic algorithm. IEEE Trans. Syst. Man Cybern. 24, 656–667 (1994)
S. Geman, D. Geman, Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984)
S. Bandyopadhyay, U. Maulik, M.K. Pakhira, Clustering using simulated annealing with probabilistic redistribution. Int. J. Pattern Recognit. Artif. Intell. 15(2), 269–285 (2001)
U. Maulik, S. Bandyopadhyay, J. Trinder, SAFE: an efficient feature extraction technique. J. Knowl. Inf. Syst. 3, 374–387 (2001)
P. Czyzak, A. Jaszkiewicz, Pareto simulated annealing – a metaheuristic technique for multiple-objective combinatorial optimization. J. Multicriteria Decis. Anal. 7, 34–47 (1998)
D.K. Nam, C. Park, Multiobjective simulated annealing: a comparative study to evolutionary algorithms. Int. J. Fuzzy Syst. 2(2), 87–97 (2000)
E.L. Ulungu, J. Teghaem, P. Fortemps, D. Tuyttens, MOSA method: a tool for solving multiobjective combinatorial decision problems. J. Multi-Criteria Decis. Anal. 8, 221–236 (1999)
Y. Shi, R. Eberhart, Parameter selection in particle swarm optimization, in Evolutionary Programming VII: Proceedings of EP 98, San Diego, 1998, pp. 591–600
http://www.scholarpedia.org/article/Particle_swarm_optimization, 2011
M. Clerc, J. Kennedy, The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
P. Suganthan, Particle swarm optimizer with neighborhood optimizer, in Proceedings of the Congress on Evolutionary Computation, Washington, DC, 1999, pp. 1958–1962
Y. Shi, R. Eberhart, A modified particle swarm optimizer, in Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, AK, NJ, 1998, pp. 69–73
J. Kennedy, Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance, in Proceedings of the Congress on Evolutionary Computation, Washington, DC, 1999, pp. 1931–1938
J. Kennedy, R. Mendes, Population structure and particle performance, in Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu, 2002
A.P. Engelbrecht, Fundamentals of Computational Swarm Intelligence (Wiley, Chichester, 2005)
R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization: an overview. Swarm Intell. 1(1), 33–57 (2007)
J. Kennedy, R. Eberhart, A discrete binary version of the particle swarm algorithm, in Proceedings of IEEE International Conference on Systems, Man, Cybernetics, Computational Cybernetics, Simulation, vol. 5 (IEEE Service Center, Piscataway, NJ, 1997), Orlando, 1997, pp. 4104–4109
J. Kennedy, Bare bones particle swarms, in Proceedings of the IEEE Swarm Intelligence Symposium (SIS 2003), Indianapolis, IN, USA, 2003, pp. 80–87
R. Mendes, J. Kennedy, J. Neves, The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)
F.V. den Bergh, An Analysis of Particle Swarm Optimizers. Ph.D. thesis, Department of Computer Science, University of Pretoria, 2002
I.C. Trelea, The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)
R. Poli, Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. Appl. 2008, 1–10 (2008). no. 685175
A. Konak, D.W. Coit, A.E. Smith, Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91, 992–1007 (2006)
J.D. Schaffer, Multiple objective optimization with vector evaluated genetic algorithms, in Genetic Algorithms and Their Applications: Proceedings of the First International Conference on Genetic Algorithms and Their Applications, (Lawrence Erlbaum, Hillsdale, 1985), pp. 93–100
C.M. Fonseca, P.J. Fleming, Multiobjective genetic algorithms, in Proceedings of IEE Colloquium on Genetic Algorithms for Control Systems Engineering, No. 1993/130, London, 28 May 1993
J. Horn, N. Nafpliotis, D.E. Goldberg, A niched Pareto genetic algorithm for multiobjective optimization, in Proceedings of the First IEEE Conference on Evolutionary Computation IEEE World Congress on Computational Intelligence, Orlando, 27–29 June 1994
P. Hajela, C.-Y. Lin, Genetic search strategies in multicriterion optimal design. Struct. Optim. 4(2), 99–107 (1992)
N. Srinivas, K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms. J. Evol. Comput. 2(3), 221–248 (1994)
E. Zitzler, L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)
E. Zitzler, M. Laumanns, L. Thiele, SPEA2: improving the strength Pareto evolutionary algorithm, Technical report, Swiss Federal Institute of Techonology, Zurich, 2001
J.D. Knowles, D.W. Corne, Approximating the nondominated front using the Pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2000)
D.W. Corne, J.D. Knowles, M.J. Oates, The Pareto envelope-based selection algorithm for multiobjective optimization, in Proceedings of Sixth International Conference on Parallel Problem Solving from Nature, Paris, 18–20 Sept 2000 (Springer, 2000)
D.W. Corne, N.R. Jerram, J. Knowles, J. Oates, PESA-II: region-based selection in evolutionary multiobjective optimization, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), San Francisco, 2001
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
R. Sarker, K.-H. Liang, C. Newton, A new multiobjective evolutionary algorithm. Eur. J. Oper. Res. 140(1), 12–23 (2002)
C.A. Coello Coello, G.T. Pulido, A micro-genetic algorithm for multiobjective optimization: evolutionary multi-criterion optimization, in First International Conference, EMO 2001, Zurich, 7–9 Mar 2001 (Springer, 2001)
H. Lu, G.G. Yen, Rank-density-based multiobjective genetic algorithm and benchmark test function study. IEEE Trans. Evol. Comput. 7(4), 325–343 (2003)
G.G. Yen, H. Lu, Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation. IEEE Trans. Evol. Comput. 7(3), 253–274 (2003)
E. Zitzler, K. Deb, L. Thiele, Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
F. Kursawe, A variant of evolution strategies for vector optimization, in Parallel Problem Solving from Nature. First Workshop, PPSN 1 Proceedings, Dortmund, 1–3 Oct 1991 (Springer, 1991)
D.E. Goldberg, J. Richardson, Genetic algorithms with sharing for multimodal function optimization, in Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, Cambridge, 28–31 July 1987 (Lawrence Erlbaum Associates, 1987)
K. Deb, D.E. Goldberg, An investigation of niche and species formation in genetic function optimization, in Proceedings of the Third International Conference on Genetic Algorithms, ed. by J.D. Schaffer (Morgan Kaufmann, USA, 1989), pp. 42–50
M.T. Jensen, Reducing the run-time complexity of multiobjective EAs: the NSGA-II and other algorithms. IEEE Trans. Evol. Comput. 7(5), 503–515 (2003)
A. Konak, A.E. Smith, Multiobjective optimization of survivable networks considering reliability, in Proceedings of the 10th International Conference on Telecommunication Systems, Naval Postgraduate School, Monterey, 2002
A. Konak, A.E. Smith, Capacitated network design considering survivability: an evolutionary approach. J. Eng. Optim. 36(2), 189–205 (2004)
J.N. Morse, Reducing the size of the nondominated set: pruning by clustering. Comput. Oper. Res. 7(1–2), 55–66 (1980)
H. Ishibuchi, T. Murata, Multi-objective genetic local search algorithm, in Proceedings of the IEEE International Conference on Evolutionary Computation, Nagoya, 20–22 May 1996
H. Lu, G.G. Yen, Rank-density based multiobjective genetic algorithm, in Proceedings of the 2002 World Congress on Computational Intelligence, WCCI’02, Honolulu, 12–17 May 2002
F. Jimenez, A.F. Gomez-Skarmeta, G. Sanchez, K. Deb, An evolutionary algorithm for constrained multi-objective optimization, in Proceedings of the 2002 World Congress on Computational Intelligence, WCCI’02, Honolulu, 12–17 May 2002
F. Jimenez, J.L. Verdegay, A.F. Gomez-Skarmeta, Evolutionary techniques for constrained multiobjective optimization problems, in Workshop on Multi-criterion Optimization Using Evolutionary Methods GECCO-1999, Orlando, Florida, USA, 1999
E. Mezura-Montes, M. Reyes-Sierra, C.A. Coello Coello, Multi-objective optimization using differential evolution: a survey of the state-of-the-art, in Advances in Differential Evolution, ed. by U.K. Chakraborty (Springer, Berlin, 2008), pp. 173–196
B.V. Babu, M.M.L. Jehan, Differential evolution for multiobjective optimization, in Proceedings of the 2003 Congress on Evolutionary Computation (CEC’2003), Canberra, vol. 4 (IEEE, 2003), pp. 2696–2703
K. Deb, Multi-objective Optimization Using Evolutionary Algorithms (Wiley, Chichester, 2001)
H. Li, Q. Zhang, A multiobjective differential evolution based on decomposition for multiobjective optimization with variable linkages, in 9th International Conference on Parallel Problem Solving from Nature – PPSN IX, Reykjavik, ed. by T.P. Runarsson, H.-G. Beyer, E. Burke, J.J. Merelo-Guervós, L.D. Whitley, X. Yao. Volume 4193 of Lecture Notes in Computer Science (Springer, 2006), pp. 583–592
C.S. Chang, D.Y. Xu, H.B. Quek, Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mass transit system. IEE Proc. Electr. Power Appl. 146(5), 577–583 (1999)
H.A. Abbass, R. Sarker, C. Newton, PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems, in Proceedings of the Congress on Evolutionary Computation 2001 (CEC’2001), vol. 2 (IEEE Service Center, Piscataway, NJ, 2001), pp. 971–978
H.A. Abbass, R. Sarker, The Pareto differential evolution algorithm. Int. J. Artif. Intell. Tools 11(4), 531–552 (2002)
R. Sarker, H. Abbass, C. Newton, Solving two multi-objective optimization problems using evolutionary algorithm, in Computational Intelligence in Control, ed. by M. Mohammadian, R. Sarker, X. Yao (Idea Group Publishing, Hershey, USA, 2002), pp. 218–232
H.A. Abbass, The self-adaptive Pareto differential evolution algorithm, in Proceedings of Congress on Evolutionary Computation (CEC’2002), vol. 1 (IEEE Service Center, Piscataway, 2002), pp. 831–836
H.A. Abbass, A memetic Pareto evolutionary approach to artificial neural networks, in Proceedings of the fourteenth Australian Joint Conference on Artificial Intelligence, Adelaide, Australia, ed. by M. Brooks, D. Corbett, M. Stumptner. Volume 2256 of Lecture Notes in Computer Science (Springer, 2001), pp. 1–12
J. Lampinen, DE’s selection rule for multiobjective optimization, Technical Report, Department of Information Technology, Lappeenranta University of Technology, 2001
S. Kukkonen, J. Lampinen, An extension of generalized differential evolution for multi-objective optimization with constraints, in Parallel Problem Solving from Nature – PPSN VIII, Birmingham. Volume 3242 of Lecture Notes in Computer Science (Springer, 2004), pp. 752–761
L.V. Santana-Quintero, C.A. Coello Coello, An algorithm based on differential evolution for multi-objective problems. Int. J. Comput. Intell. Res. 1(2), 151–169 (2005)
M. Laumanns, L. Thiele, K. Deb, E. Zitzler, Combining convergence and diversity in evolutionary multi-objective optimization. Evol. Comput. 10, 263–282 (Fall 2002)
E.A.P. Flores, Integración Simultánea de Aspectos Estructurales y Dinámicos para el Diseño Óptimo de un Sistema de Transmisión de Variación Continua. Ph.D. thesis, Departamento de Ingeniería Eléctrica, Sección de Mecatrónica, CINVESTAV-IPN, México, 2006
E. Mezura-Montes, C.A. Coello Coello, A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Trans. Evol. Comput. 9, 1–17 (2005)
N.K. Madavan, Multiobjective optimization using a Pareto differential evolution approach, in Congress on Evolutionary Computation (CEC’2002), vol. 2 (IEEE Service Center, Piscataway, 2002), pp. 1145–1150
K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, in Proceedings of the Parallel Problem Solving from Nature VI Conference, Paris, ed. by M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo, H.-P. Schwefel. Lecture Notes in Computer Science, vol. 1917 (Springer, 2000), pp. 849–858
A.W. Iorio, X. Li, Solving rotated multi-objective optimization problems using differential evolution, in Proceedings of Australian Conference on Artificial Intelligence: Advances in Artificial Intelligence, Cairns, ed. by G.I. Webb, X. Yu. Volume 3339 of Lecture Notes in Artificial Intelligence (Springer-Verlag, Berlin, Heidelberg, 2004), pp. 861–872
A.W. Iorio, X. Li, Incorporating directional information within a differential evolution algorithm for multi-objective optimization, in Proceedings of 2006 Genetic and Evolutionary Computation Conference (GECCO’2006), Seattle, ed. by M. Keijzer et al., vol. 1 (ACM, 2006), pp. 691–697
T. Robiic, B. Filipic, DEMO: differential evolution for multiobjective optimization, in Proceedings of Third International Conference on Evolutionary Multi-criterion Optimization (EMO 2005), Guanajuato, ed. by C.A. Coello Coello, A.H. Aguirre, E. Zitzler. Volume 3410 of Lecture Notes in Computer Science (Springer, 2005), pp. 520–533
S. Kukkonen, J. Lampinen, GDE3: the third evolution step of generalized differential evolution, in Proceedings of 2005 IEEE Congress on Evolutionary Computation (CEC’2005), Edinburgh, vol. 1 (IEEE Service Center, 2005), pp. 443–450
K.E. Parsopoulos, D.K. Taoulis, N.G. Pavlidis, V.P. Plagianakos, M.N. Vrahatis, Vector evaluated differential evolution for multiobjective optimization, in Proceedings of 2004 Congress on Evolutionary Computation (CEC’2004), Portland, vol. 1 (IEEE Service Center, 2004), pp. 204–211
A.G. Hernández-Díaz, L.V. Santana-Quintero, C.A. Coello Coello, R. Caballero, J. Molina, A new proposal for multiobjective optimization using differential evolution and rough sets theory, in Proceedings of 2006 Genetic and Evolutionary Computation Conference (GECCO’2006), Seattle, ed. by M. Keijzer et al., vol. 1 (ACM, 2006), pp. 675–682
A.G. Hernández-Díaz, L.V. Santana-Quintero, C.A. Coello Coello, J. Molina, Pareto-adaptive ε-dominance. Evol. Comput. 15, 493–517 (Winter 2007)
R.L. Becerra, C.A. Coello Coello, Solving hard multiobjective optimization problems using ε-constraint with cultured differential evolution, in Proceedings of 9th International Conference on Parallel Problem Solving from Nature – PPSN IX, Reykjavik, ed. by T.P. Runarsson, H.-G. Beyer, E. Burke, J.J. Merelo-Guervos, L.D. Whitley, X. Yao. Volume 4193 of Lecture Notes in Computer Science (Springer, 2006), pp. 543–552
Y.Y. Haimes, L.S. Lasdon, D.A. Wismer, On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Trans. Syst. Man Cybern. 1, 296–297 (1971)
R.L. Becerra, C.A. Coello Coello, Cultured differential evolution for constrained optimization. Comput. Methods Appl. Mech. Eng. 195, 4303–4322 (2006)
K.C. Srigiriraju, Noninferior Surface Tracing Evolutionary Algorithm (NSTEA) for Multiobjective Optimization. Master’s Thesis, North Carolina State University, Raleigh, Aug 2000
S.R. Ranjithan, S.K. Chetan, H.K. Dakshima, Constraint method-based evolutionary algorithm (CMEA) for multiobjective optimization, in Proceedings of First International Conference on Evolutionary Multi-criterion Optimization, Zurich, Switzerland, ed. by E. Zitzler, K. Deb, L. Thiele, C.A. Coello Coello, D. Corne. Lecture Notes in Computer Science, vol. 1993, pp. 299–313 (Springer, 2001)
I. Das, J. Dennis, A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Struct. Opt. 14(1), 163–169 (1997)
A. Jaszkiewicz, Comparison of local search-based metaheuristics on the multiple objective knapsack problem. Found. Comput. Decis. Sci. 26(1), 99–120 (2001)
B. Suman, Study of self-stopping PDMOSA and performance measure in multiobjective optimization. Comput. Chem. Eng. 29, 1131–1147 (2005)
B. Suman, P. Kumar, A survey of simulated annealing as a tool for single and multiobjective optimization. J. Oper. Res. Soc. 57(10), 1143–1160 (2006)
B.J.T. Fernandes, G.D.C. Cavalcanti, T.I. Ren, Classification and segmentation of visual patterns based on receptive and inhibitory fields, in Proceedings of the 8th International Conference on Hybrid Intelligent Systems, Barcelona, 2008, pp. 126–131
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Bhattacharyya, S., Maulik, U. (2013). Introduction. In: Soft Computing for Image and Multimedia Data Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40255-5_1
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