Engelbrecht Andries P. Computational Intelligence: An Introduction
Подождите немного. Документ загружается.
488 REFERENCES
[15] H. Akaike. A New Look at Statistical Model Identification. IEEE Transactions
on Automatic Control, 19(6):716–723, 1974.
[16] B. Al-Kazemi and C.K. Mohan. Multi-Phase Discrete Particle Swarm Optimiza-
tion. In Proceedings of the International Workshop on Frontiers in Evolutionary
Algorithms, pages 622–625, 2002.
[17] B. Al-Kazemi and C.K. Mohan. Multi-Phase Generalization of the Particle
Swarm Optimization Algorithm. In Proceedings of the IEEE Congress on Evo-
lutionary Computation, pages 489–494, 2002.
[18] B. Al-Kazemi and C.K. Mohan. Training Feedforward Neural Networks us-
ing Multi-Phase Particle Swarm Optimization. In Proceedings of the Nineth
International Conference on Neural Information Processing, volume 5, pages
2615–2619, 2002.
[19] M.M. Ali and A. T¨orn. Population Set-Based Global Optimization Algorithms:
Some Modifications and Numerical Studies. Computers & Operations Research,
31(10):1703–1725, 2004.
[20] G.N. Aly and A.M. Sameh. Evolution of Recurrent Cascade Correlation Net-
works with Distributed Collaborative Species. In Proceedings of the IEEE Sym-
posium on Combinations of Evolutionary Computation, pages 240–249, 2000.
[21] S. Amari, N. Murata, K-R. M¨uller, M. Finke, and H. Yang. Asymptotic Sta-
tistical Theory of Overtraining and Cross-Validation. Technical Report METR
95-06, Department of Mathematical Engineering and Information, University of
Tokyo, 1995.
[22] S. Amari, N. Murata, K-R. M¨uller, M. Finke, and H. Yang. Statistical Theory
of Overtraining – Is Cross-Validation Asymptotically Effective? In D.S. Touret-
zky, M.C. Mozer, and M.E. Hasselmo, editors, Advances in Neural Information
Processing Systems, volume 8, pages 176–182, 1996.
[23] M.R. Anderberg. Cluster Analysis for Applications. Academic Press, New York,
1973.
[24] P.J. Angeline. Adaptive and Self-Adaptive Evolutionary Computation. In
M. Palaniswami and Y. Attikiouzel, editors, Computational Intelligence: A Dy-
namic Systems Perspective, pages 152–163, 1995.
[25] P.J. Angeline. Evolutionary Optimization versus Particle Swarm Optimization:
Philosophy and Performance Differences. In Proceedings of the Seventh Annual
Conference on Evolutionary Programming, pages 601–610, 1998.
[26] P.J. Angeline. Using Selection to Improve Particle Swarm Optimization. In
Proceedings of the IEEE Congress on Evolutionary Computation, pages 84–89,
1998.
[27] P.J. Angeline and J.B. Pollack. Competitive Environments Evolve Better Solu-
tions for Complex Tasks. In Proceedings of the Fifth International Conference
on Genetic Algorithms, pages 264–270, 1993.
[28] P.J. Angeline, G.M. Saunders, and J.P. Pollack. An Evolutionary Algorithm
That Constructs Recurrent Neural Networks. IEEE Transactions on Neural
Networks, 5(1):54–65, 1994.
[29] R. Annaluru, S. Das, and A. Pahwa. Multi-Level Ant Colony Algorithm for Op-
timal Placement of Capacitors in Distribution Systems. In CEC2004. Congress
on Evolutionary Computation, volume 2, pages 1932–1937, June 2004.
REFERENCES 489
[30] M.J. Antunes and M.E. Correia. Towards a New Immunity-Inspired Intrusion
Detection Framework. Technical Report DCC-2006-04, Departamento de Cien-
cia de Computadores Faculdade de Ciencias da Universidade do Porto, 2006.
[31] D.V. Arnold. Evolution Strategies with Adaptively Rescaled Mutation Vectors.
Technical Report CS-2005-04, Dalhousie University, 2005.
[32] D.V. Arnold and H-G. Beyer. A General Noise Model and Its Effects on Evolu-
tion Strategy Performance. IEEE Transactions on Evolutionary Computation,
10(4):380–391, 2006.
[33] R. Axelrod. Evolution of Strategies in The Iterated Prisoner’s Dilemma. In
L. Davis, editor, Genetic Algorithms and Simulated Annealing, pages 32–41.
Morgan Kaufmann, 1987.
[34] S. Aydin and H. Temeltas. Time Optimal Trajectory Planning for Mobile Robots
by Differential Evolution Algorithm and Neural Networks. In Proceedings of the
IEEE International Symposium on Industrial Electronics, volume 1, pages 352–
357, 2003.
[35] B.V. Babu and M.M.L. Jehan. Differential Evolution for Multi-Objective Opti-
mization. In Proceedings of the IEEE Congress on Evolutionary Computation,
volume 4, pages 2696–2703, 2003.
[36] B.V. Babu and K.K.N. Sastry. Estimation of Heat Transfer Parameters in A
Trickle-Bed Reactor using Differential Evolution and Orthogonal Collocation.
Computers & Chemical Engineering, 23(3):327–339, 1999.
[37] V. Bachelet and E-G. Talbi. COSEARCH: A Co-Evolutionary Metaheuristic.
In Proceedings of the IEEE Congress on Evolutionary Computation,volume2,
pages 1550–1557, 2000.
[38] T. B¨ack. Selective Pressure in Evolutionary Algorithms: A Characterization of
Selection Mechanisms. In Proceedings of the First IEEE Conference on Evolu-
tionary Computation, pages 57–62, 1994.
[39] T. B¨ack. Evolutionary Algorithms in Theory a nd Practice. Oxford University
Press, New York, 1996.
[40] T. B¨ack. On The Behavior of Evolutionary Algorithms in Dynamic Environ-
ments. In IEEE World Congress on Evolutionary Computation, Proceedings of
the IEEE Congress on Evolutionary Computation, pages 446–451, 1998.
[41] T. B¨ack, D.B. Fogel, and Z. Michalewicz. Evolutionary Computation 2: Ad-
vanced Algorithms and Operators. IOP Press, 2000.
[42] T. B¨ack and U. Hammel. Evolution Strategies Applied to Perturbed Objective
Functions. In IEEE World Congress on Computational Intelligence, Proceedings
of the IEEE Conference on Evolutionary Computation, volume 1, pages 40–45,
1994.
[43] T. B¨ack, F. Hoffmeister, and H-P. Schwefel. A Survey of Evolution Strategies.
In L.B. Belew and R.K. Booker, editors, Proceedings of the Fourth International
Conference on Genetic Algorithms, pages 2–9, 1991.
[44] T. B¨ack and M. Sch¨utz. Evolution Strategies for Mixed-Integer Optimization
of Optical Multilayer Systems. In J.R. McDonnell, R.G. Reynolds, and D.B.
Fogel, editors, Proceedings of the Fourth Annual Conference on Evolutionary
Programming, pages 35–51, Cambridge, M.A., 1995.
490 REFERENCES
[45] T. B¨ack and H.-P. Schwefel. An Overview of Evolutionary Algorithms for Pa-
rameter Optimization. Evolutionary Computation, 1(1):1–23, 1993.
[46] J.E. Baker. Reducing Bias and Inefficiency in The Selection Algorithm. In
J. Grefenstette, editor, Proceedings of the Second International Conference of
Genetic Algorithms, pages 14–21, Hillsdale, N.J., 1987. Erlbaum.
[47] P. Baldi. Computing with Arrays of Bell-Shaped and Sigmoid Functions. In
R.P. Lippmann, J.E. Moody, and D.S. Touretzky, editors, Neural Information
Processing Systems, volume 3, pages 735–742, San Mateo, C.A., 1991. Morgan
Kaufmann.
[48] W. Banzhaf. Interactive Evolution. In Handbook of Evolutionary Computation,
pages C2.10:1–C2.10:6. IOP Press, 1997.
[49] B. Bar´an and M. Schaerer. A Multiobjective Ant Colony System for Vehicle
Routing Problem with Time Windows. In Proceedings of the Twenty First
IASTED International Conference on Applied Informatics, pages 97–102, 2003.
[50] E. Barnard. Performance and Generalization of the Classification Figure of Merit
Criterion Function. IEEE Transactions on Neural Networks, 2(2):322–325, 1991.
[51] R. Battiti. First- and Second-Order Methods for Learning: Between Steepest
Descent and Newton’s Method. Neural Computation, 4:141–166, 1992.
[52] D.C. Bauer, J. Cannady, and R.C. Garcia. Detecting Anomalous Behavior:
Optimization of Network Traffic Parameters via An Evolution Strategy. In Pro-
ceedings of the IEEE Southeast Conference, pages 34–39, 2001.
[53] E.B. Baum. On the Capabilities of Multilayer Perceptrons. Journal of Complex-
ity, 4:193–215, 1988.
[54] E.B. Baum and D. Haussler. What Size Net Gives Valid Generalization? In
D.S. Touretzky, editor, Advances in Neural Information Processing Systems,
volume 1, pages 81–90, 1989.
[55] D. Beasley, D.R. Bull, and R.R. Martin. A Sequential Niching Technique for
Multimodal Function Optimization. Evolutionary Computation, 1(2):101–125,
1993.
[56] R.L. Becerra and C.A. Coello Coello. Culturizing Differential Evolution for
Constrained Optimization. In Proceedings of the Fifth Mexican International
Conference in Computer Science, pages 304–311, 2004.
[57] S. Becker and Y. Le Cun. Improving The Convergence of Back-Propagation
Learning with Second Order Methods. In D.S. Touretzky, G.E. Hinton, and
T.J. Sejnowski, editors, Proceedings of the 1988 Connectionist Summer School,
pages 29–37. Morgan Kaufmann, 1988.
[58] L.M. Belue and K.W. Bauer. Determining Input Features for Multilayer Per-
ceptrons. Neurocomputing, 7:111–121, 1995.
[59] A. Berlanga, P. Isasi, A. Sanchis, and J.M. Molina. Neural Networks Robot Con-
troller Trained with Evolution Strategies. In Proceedings of the IEEE Congress
on Evolutionary Computation, pages 413–419, 1999.
[60] A. Berlanga, A. Sanchis, P. Isasi, and J.M. Molina. A General Learning Co-
Evolution Method to Generalize Autonomous Robot Navigation Behavior. In
Proceedings of the IEEE Congress on Evolutionary Computation,volume1,
pages 769–776, 2000.
REFERENCES 491
[61] H-G. Beyer. Toward a Theory of ‘Evolution Strategies’. Some Asymptotical
Results from The (1
+
,λ)-Theory. Evolutionary Computation, 1(2):165–188,
1993.
[62] H-G. Beyer. Toward a Theory of Evolution Strategies: On The Benefits of Sex
–The(µ/µ, λ)Theory.Evolutionary Computation, 3(1):81–111, 1995.
[63] H-G. Beyer. Toward a Theory of Evolution Strategies: The (µ, λ)-Theory. Evo-
lutionary Computation, 2(4):381–407, 1995.
[64] H-G. Beyer. Toward a Theory of Evolution Strategies: Self-Adaptation. Evolu-
tionary Computation, 3(3):311–347, 1996.
[65] H-G. Beyer. Mutate Large, but Inherit Small! On the Analysis of Rescaled Mu-
tations in (1,λ)-ES with Noisy Fitness Data. In A.E. Eiben, T. B¨ack, M. Schoe-
nauer, and H-P. Schwefel, editors, Proceedings of the Parallel Problem Solving
from Nature Conference, pages 109–118. Springer Verlag, 1998.
[66] Z. Bian, Y. Yu, B. Zheng, M. Wang, and H. Mao. A Novel Evolution Strategy
Algorithm Based on the Selected Direction by The Polar Coordinates. In In-
ternational Symposium on Systems and Control in Aerospace and Astronautics,
pages 907–911, 2006.
[67] G. Bilchev and I.C. Parmee. The Ant Colony Metaphor for Searching Continuous
Design Spaces. In T. Fogarty, editor, Proceedings of the AISB Workshop on
Evolutionary Computation, Lecture Notes in Computer Science, volume 993,
pages 25–39. Springer-Verlag, 1995.
[68] G. Bilchev and I.C. Parmee. Constrained Optimisation with an Ant Colony
Search Model. In Proceedings of Adaptive Computing in Engineering Design
and Control, pages 145–151, 1996.
[69] H.K. Birru. Empirical Study of Two Classes of Bit Variation Operators in Evo-
lutionary Computation. In Proceedings of the IEEE Congress on Evolutionary
Computation, volume 3, 1999.
[70] H.K. Birru, K. Chellapilla, and S.S. Rao. Local Search Operators in Fast Evo-
lutionary Programming. In Proceedings of the IEEE Congress on Evolutionary
Computation, volume 2, 1999.
[71] C. Bishop. Exact Calculation of the Hessian Matrix for the Multilayer Percep-
tron. Neural Computation, 4:494–501, 1992.
[72] T.M. Blackwell. Particle Swarms and Population Diversity II: Experiments. In
Genetic and Evolutionary Computation Conference, Workshop on Evolutionary
Algorithms for Dynamic Optimization Problems, pages 14–18, 2003.
[73] T.M. Blackwell. Swarms in Dynamic Environments. In Proceedings of the Ge-
netic and Evolutionary Computation Conference, Lecture Notes in Computer
Science, volume 2723, pages 1–12, 2003.
[74] T.M. Blackwell and P.J. Bentley. Don’t Push Me! Collision-Avoiding Swarms.
In Proceedings of the IEEE Congress on Evolutionary Computation,volume2,
pages 1691–1696, 2002.
[75] T.M. Blackwell and P.J. Bentley. Dynamic Search with Charged Swarms. In
Proceedings of the Genetic and Evolutionary Computation Conference, pages
19–26, 2002.
492 REFERENCES
[76] C. Blum. Beam-ACO – Hybridizing Ant Colony Optimization with Beam Search:
An Application to Open Shop Scheduling. Computers and Operations Research,
32(6):1565–1591, 2004.
[77] E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural
to Artificial Systems. Oxford University Press, 1999.
[78] E. Bonabeau, G. Theraulaz, V. Fourcassi´e, and J-L. Deneubourg. The Phase-
Ordering Kinetics of Cemetery Organization in Ants. Physical Review E,
57:4568–4571, 1998.
[79] S. B¨os. Optimal Weight Decay in a Perceptron. In Proceedings of the Interna-
tional Conference on Artificial Neural Networks, pages 551–556, 1996.
[80] H.M. Botee and E. Bonabeau. Evolving Ant Colony Optimization. Advanced in
Complex Systems, 1:149–159, 1998.
[81] D. Braendler and T. Hendtlass. The Suitability of Particle Swarm Optimization
for Training Neural Hardware. In Proceedings of the Fifteenth International
Conference on Industrial and Engineering, Applications of Artificial Intelligence
and Expert Systems, Lecture Notes in Computer Science, volume 2358, pages
190–199. Springer-Verlag, 2002.
[82] J. Branke. Memory Enhanced Evolutionary Algorithm for Changing Optimiza-
tion Problems. In Proceedings of the IEEE Congress on Evolutionary Compu-
tation, volume 3, pages 1875–1882, 1999.
[83] J. Branke. Evolutionary Optimization in Dynamic Environments. Springer, 2001.
[84] L. Breiman. Bagging Predictors. Machine Learning, 24(2):123–140, 1996.
[85] H. Bremermann, M. Rogson, and S. Salaff. Global Properties of Evolution
Processess. In H. Pattee, E. Edlsack, L. Fein, and A. Callahan, editors, Natural
Automata and Useful Simulations, pages 3–41, Washington, D.C., 1966. Spartan
Books.
[86] H.J. Bremermann. Optimization through Evolution and Recombination. In M.C.
Yovits, G.T. Jacobi, and G.D. Goldstine, editors, Self-Organization Systems,
pages 93–106. Spartan Books, 1962.
[87] P. Bretscher and M. Cohn. A Theory of Self-Nonself Discrimination. Science,
169:1042–1049, 1970.
[88] R. Brits. Niching Strategies for Particle Swarm Optimization. Master’s thesis,
Department of Computer Science, University of Pretoria, 2002.
[89] R. Brits, A.P. Engelbrecht, and F. van den Bergh. A Niching Particle Swarm
Optimizer. In Proceedings of the Fourth Asia-Pacific Conference on Simulated
Evolution and Learning, pages 692–696, 2002.
[90] R. Brits, A.P. Engelbrecht, and F. van den Bergh. Solving Systems of Uncon-
strained Equations using Particle Swarm Optimization. In Proceedings of the
IEEE Conference on Systems, Man, and Cybernetics, volume 3, pages 102–107,
Oct 2002.
[91] R. Brits, A.P. Engelbrecht, and F. van den Bergh. Locating Multiple Optima
using Particle Swarm Optimization. Applied Mathematics and Computation,
2007.
[92] D.S. Broomhead and D. Lowe. Multivariate Functional Interpolation and Adap-
tive Networks. Complex Systems, 2:321–355, 1988.
REFERENCES 493
[93] M.D. Bugajska and A.C. Schultz. Anytime Coevolution of Form and Function.
In Proceedings of the IEEE Congress on Evolutionary Computation,volume1,
pages 359–366, 2003.
[94] B. Bullnheimer, G. Kotsis, and C. Strauss. Parallelization Strategies for The Ant
System. In G. Toraldo A. Murli, P. Pardalos, editor, Kluwer Series on Applied
Optimization, pages 87–100, 1997.
[95] W.L. Buntine and A.S. Weigend. Computing Second Order Derivatives in
Feed-Forward Networks: A Review. IEEE Transactions on Neural Networks ,
5(3):480–488, 1994.
[96] F.M. Burnet. The Clonal Selection Theory of Acquired Immunity. Vanderbilt
University Press, Nashville, T.N., 1959.
[97] F.M. Burnet. Clonal Selection and After. In G.I. Bell, A.S. Perelson, and
G.H. Pimbley Jr., editors, Theoretical Immunology, pages 63–85. Marcel Dekker
Inc., New York, 1978.
[98] P. Burrascano. A Pruning Technique Maximizing Generalization. In Proceedings
of the International Joint Conference on Neural Networks, volume 1, pages 347–
350, 1993.
[99] D. Camara and A.A.F. Loureiro. A GPS/Ant-Like Routing Algorithm for Ad
Hoc Networks. In Proceedings of the IEEE Wireless Communications and Net-
working Conference, pages 1232–1236, 2000.
[100] E. Cant´u-Paz. A Survey of Parallel Genetic Algorithms. Calculateurs Paralleles,
Reseaux et Systems Repartis, 10(2):141–171, 1998.
[101] E. Cant´u-Paz. Migration Policies and Takeover Times in Parallel Genetic Al-
gorithms. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar,
M. Jakiela, and R.E. Smith, editors, Proceedings of the Genetic and Evolu-
tionary Computation Conference, page 775, San Francisco, C.A., 1999. Morgan
Kaufmann.
[102] E. Cant´u-Paz. Migration Policies, Selection Pressure, and Parallel Evolutionary
Algorithms. Technical Report IlliGAL Report No. 99015, University of Illinois
at Urbana-Champaign, 1999.
[103] E. Cant´u-Paz. Parallel Genetic Algorithms with Distributed Panmictic Popu-
lations. Technical Report IlliGAL Report No. 99006, University of Illinois at
Urbana-Champaign, 1999.
[104] E. Cant´u-Paz. Topologies, Migration Rates, and Multi-Population Parallel
Genetic Algorithms. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon,
V. Honavar, M. Jakiela, and R.E. Smith, editors, Proceedings of the Genetic
and Evolutionary Computation Conference, pages 91–98, San Francisco, C.A.,
1999. Morgan Kaufmann.
[105] P. Cardoso, M. Jes´us, and A. M´arquez. MONACO – Multi-Objective Network
OptimizationBasedonanACO. InProceedings of Encuentros de Geometr
´
ıa
Computacional, 2003.
[106] A. Carlisle. Applying the Particle Swarm Optimizer to Non-Stationary Environ-
ments. PhD thesis, Auburn University, 2002.
[107] A. Carlisle and G. Dozier. Adapting Particle Swarm Optimization to Dynamic
Environments. In Proceedings of the International Conference on Artificial In-
telligence, pages 429–434, 2000.
494 REFERENCES
[108] A. Carlisle and G. Dozier. An Off-the-Shelf PSO. In Proceedings of the Workshop
on Particle Swarm Optimization, pages 1–6, 2001.
[109] A. Carlisle and G. Dozier. Tracking Changing Extrema with Adaptive Particle
Swarm Optimizer. In Proceedings of the Fifth Biannual World Automation
Congress, pages 265–270, 2002.
[110] T.D.H. Cau and R.J. Kaye. Multiple Distributed Energy Storage Scheduling
using Constructive Evolutionary Programming. In Proceedings of the Twenty-
Secondth IEEE Power Engineering Society International Conference on Power
Industry Computer Applications, pages 402–407, 2001.
[111] J.L. Ceciliano and R. Bieva. Transmission Network Planning using Evolutionary
Programming. In Proceedings of the IEEE Congress on Evolutionary Computa-
tion, volume 3, 1999.
[112] CS. Chang and D. Du. Differential Evolution Based Tuning of Fuzzy Automatic
Train Operation for Mass Rapid Transit System. IEE Proceedings of Electric
Power Applications, 147(3):206–212, 2000.
[113] T-T. Chang and H-C. Chang. Application of differential evolution to passive
shunt harmonic filter planning. In Proceedings of the Eigth International Con-
ference on Harmonics and Quality of Power, volume 1, pages 149–153, 1999.
[114] Y-P. Chang and C-J. Wu. Design of Harmonic Filters using Combined Feasible
Direction Method and Differential Evolution. In Proceedings of the International
Conference on Power System Technology, volume 1, pages 812–817, 2004.
[115] D. Chaturvedi, K. Deb, and S.K. Chakraborty. Structural Optimization using
Real-Coded Genetic Algorithms. In P.K. Roy and S.D. Mehta, editors, Proceed-
ings of the Symposium on Genetic Algorithms, pages 73–82, Dehradun, 1995.
Mahendra Pal Singh.
[116] Y. Chauvin. A Back-Propagation Algorithm with Optimal use of Hidden Units.
In D.S. Touretzky, editor, Advances in Neural Information Processing Systems,
volume 1, pages 519–526, 1989.
[117] Y. Chauvin. Dynamic Behavior of Constrained Back-Propagation Networks.
In D.S. Touretzky, editor, Advances in Neural Information Processing Systems,
volume 2, pages 642–649, 1990.
[118] K. Chellapilla. Combining Mutation Operators in Evolutionary Programming.
IEEE Transactions on Evolutionary Computation, 2(3):91–96, 1998.
[119] K. Chellapilla and D.B. Fogel. Evolution, Neural Networks, Games, and Intelli-
gence. In Proceedings of the IEEE, pages 1471–1496, 1999.
[120] K. Chellapilla and D.B. Fogel. Evolving Neural Networks to Play Checkers with-
out Expert Knowledge. IEEE Transactions on Neural Networks, 10(6):1382–
1391, 1999.
[121] K. Chellapilla and D.B. Fogel. Anaconda Defeats Hoyle 6-0: A Case Study
Competing an Evolved Checkers Program against Commercially Available Soft-
ware. In Proceedings of the IEEE Congress on Evolutionary Computation, pages
857–863, 2000.
[122] C-W. Chen, D-Z. Chen, and G-Z. Cao. An Improved Differential Evolution
Algorithm in Training and Encoding Prior Knowledge into Feedforward Net-
works with Application in Chemistry. Chemometrics and Intelligent Laboratory
Systems, 64(1):27–43, 2002.
REFERENCES 495
[123] S. Chen, S.A. Billings, C.F.N. Cowan, and P.P. Grant. Practical Identification
of NARMAX Models using Radial Basis Functions. International Journal of
Control, 52:1327–1350, 1990.
[124] J-P. Chiou and F-S. Wang. A Hybrid Method of Differential Evolution with Ap-
plication to Optimal Control Problems of A Bioprocess System. In IEEE World
Congress on Computational Intelligence, Proceedings of the IEEE International
Conference on Evolutionary Computation, pages 627–632, 1998.
[125] J-P. Chiou and F-S. Wang. Hybrid Method of Evolutionary Algorithms for
Static and Dynamic Optimization Problems with Application to A Fed-Batch
Fermentation Process. Computers & Chemical Engineering, 23(9):1277–1291,
1999.
[126] D-H. Choi and S-Y. Oh. A New Mutation Rule for Evolutionary Programming
Motivated from Backpropagation Learning. IEEE Transactions on Evolutionary
Computation, 4(2):188–190, 2000.
[127] L. Chr´etien. Organisation Spatiale du Mat
´
eriel Provenant de l’excavation du
nid chez Messor Barbarus et des Cadavres d’ouvri
`
eres chez ‘‘Lasius niger’’ (Hy-
menopterae: Formicidae). PhD thesis, Universit´e Libre de Bruxelles, 1996.
[128] C-J. Chung and R.G. Reynolds. A Testbed for Solving Optimization Prob-
lems using Cultural Algorithms. In L.J Fogel, P.J. Angeline, and T. B¨ack, edi-
tors, Proceedings of the Fifth Annual Conference on Evolutionary Programming,
pages 225–236, Cambridge, M.A., 1996. MIT Press.
[129] T. Cibas, F. Fogelman Souli´e, P. Gallinari, and S. Raudys. Variable Selection
with Neural Networks. Neurocomputing, 12:223–248, 1996.
[130] A. Cichocki and R. Unbehauen. Neural Networks for Optimization and Signal
Processing. Wiley, New York, 1993.
[131] V.A. Cicirello and S.F. Smith. Ant Colony Control for Autonomous Decentral-
ized Shop Floor Routing. In Proceedings of the Fifth International Symposium
on Autonomous Decentralized Systems, pages 383–390, 2001.
[132] J.M. Claverie, K. de Jong, and A.F. Sheta. Robust Nonlinear Control Design
using Competitive Coevolution. In Proceedings of the IEEE Congress on Evo-
lutionary Computation, volume 1, pages 403–409, 2000.
[133] M. Clerc. The Swarm and the Queen: Towards a Deterministic and Adaptive
Particle Swarm Optimization. In Proceedings of the IEEE Congress on Evolu-
tionary Computation, volume 3, pages 1951–1957, 1999.
[134] M. Clerc. Think Locally, Act Locally: The Way of Life of Cheap-PSO, An
Adaptive PSO. Technical report, http://clerc.maurice.free.fr/pso/, 2001.
[135] M. Clerc. Discrete Particle Swarm Optimization. In New Optimization Tech-
niques in Engineering, Lecture Notes in Computer Science, volume 3612.
Springer-Verlag, 2004.
[136] M. Clerc and J. Kennedy. The Particle Swarm-Explosion, Stability, and Con-
vergence in a Multidimensional Complex Space. IEEE Transactions on Evolu-
tionary Computation, 6(1):58–73, 2002.
[137] I. Cloete and J. Ludik. Increased Complexity Training. In J. Mira, J. Cabestany,
and A. Prieto, editors, International Workshop on Artificial Neural Networks,
Lecture Notes in Computer Science, pages 267–271, Berlin, 1993. Springer-
Verlag.
496 REFERENCES
[138] I. Cloete and J. Ludik. Delta Training Strategies. In IEEE World Congress on
Computational Intelligence, Proceedings of the International Joint Conference
on Neural Networks, volume 1, pages 295–298, 1994.
[139] I. Cloete and J. Ludik. Incremental Training Strategies. In Proceedings of the
International Conference on Artificial Neural Networks, volume 2, pages 743–
746, 1994.
[140] G. Coath and S.K. Halgamuge. A Comparison of Constraint-Handling Methods
for The Application of Particle Swarm Optimization to Constrained Nonlinear
Optimization Problems. In Proceedings of the IEEE Congress on Evolutionary
Computation, volume 4, pages 2419–2425, 2003.
[141] H.G. Cobb. An Investigation into The Use of Hypermutation as An Adaptive
Operator in Genetic Algorithms having Continuous, Time-Dependent Nonsta-
tionary Environments. Technical Report AIC-90-001, Naval Research Labora-
tory, Washington, D.C., 1990.
[142] J.P. Coelho, P.B. De Moura Oliveira, and J. Boa Ventura Cunha. Non-Linear
Concentration Control System Design using A New Adaptive PSO. In Proceed-
ings of the 5th Portuguese Conference on Automatic Control, 2002.
[143] J.P. Coelho, P.M. Oliveira, and J.B. Cunha. Greenhouse Air Temperature Con-
trol using the Particle Swarm Optimisation Algorithm. In Proceedings of the
Fifteenth Triennial World Congress of the International Federation of Automatic
Control, 2002.
[144] C.A. Coello Coello. Self-Adaptive Penalties for GA-Based Optimization. In
Proceedings of the IEEE Congress on Evolutionary Computation, volume 1, 1999.
[145] C.A. Coello Coello and R.L. Becerra. Constrained Optimization using an Evo-
lutionary Programming-based Cultural Algorithm. In I.C. Parmee, editor, Pro-
ceedings of the Fifth International Conference on Adaptive Computing in Design
and Manufacture, volume 5, pages 317–328. Springer-Verlag, 2002.
[146] C.A. Coello Coello and R.L. Becerra. Evolutionary Multiobjective Optimization
using a Cultural Algorithm. In Proceedings of the IEEE Swarm Intelligence
Symposium, pages 6–13, 2003.
[147] C.A. Coello Coello and M.S. Lechuga. MOPSO: A Proposal for Multiple Ob-
jective Particle Swarm Optimization. In Proceedings of the IEEE Congress on
Evolutionary Computation, volume 2, pages 1051–1056, 2002.
[148] C.A. Coello Coello, G. Toscano Pulido, and M. Salazar Lechuga. An Extension of
Particle Swarm Optimization that can Handle Multiple Objectives. In Workshop
on Multiple Objective Metaheuristics, 2002.
[149] C.A. Coello Coello, D.A. van Veldhuizen, and G.B. Lamont. Evolutionary Algo-
rithms for Solving Multi-Objective Problems. Plenum US, 2002.
[150] C.A. Coello Coello, D.A. Van Veldhuizen, and G.B. Lamont. Evolutionary Al-
gorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers,
2002.
[151] D. Cohn, L. Atlas, and R. Ladner. Improving Generalization with Active Learn-
ing. Machine Learning, 15:201–221, 1994.
[152] D. Cohn and G. Tesauro. Can Neural Networks do Better than the Vapnik-
Chervonenkis Bounds? In R. Lippmann, J. Moody, and D.S. Touretzky, editors,
Advances in Neural Information Processing Systems, volume 3, pages 911–917,
1991.
REFERENCES 497
[153] D.A. Cohn. Neural Network Exploration using Optimal Experiment Design.
Technical Report AI Memo No 1491, Artificial Intelligence Laboratory, Mas-
sachusetts Institute of Technology, 1994.
[154] D.A. Cohn, Z. Ghahramani, and M.I. Jordan. Active Learning with Statistical
Models. Journal of Artificial Intelligence Research, 4:129–145, 1996.
[155] A. Conradie, R. Miikkulainen, and C. Aldrich. Adaptive Control Utilizing Neu-
ral Swarming. In Proceedings of the Genetic and Evolutionary Computation
Conference, pages 60–67, 2002.
[156] J. Conradie and A.P. Engelbrecht. Training Bao Game-Playing Agents using
Coevolutionary Particle Swarm Optimization. In Proceedings of the IEEE Sym-
posium on Computational Intelligence in Games, pages 67–74, 2006.
[157] D. Corne, M. Dorigo, and F. Glover. New Ideas in Optimization. McGraw-Hill,
1999.
[158] M. Cosnard, P. Koiran, and H. Paugam-Moisy. Complexity Issues in Neural
Network Computations. In I. Simon, editor, Proceedings of the First Latin
American Symposium on Theoretical Informatics, Lecture Notes in Computer
Science, volume 583, pages 530–543. Springer-Verlag, 1992.
[159] L. Costa and P. Oliveira. An Evolution Strategy for Multiobjective Optimization.
In Proceedings of the IEEE Congress on Evolutionary Computation,volume1,
pages 97–102, 2002.
[160] M. Cottrell, B. Girard, Y. Girard, M. Mangeas, and C. Muller. SSM: A Statisti-
cal Stepwise Method for Weight Elimination. In Proceedings of the International
Conference on Artificial Neural Networks, volume 1, pages 681–684, 1994.
[161] T. Coudert, P. Berruet, and J-L. Philippe. Integration of Reconfiguration in
Transitic Systems: An Agent-Based Approach. In Proceedings of the IEEE
International Conference on Systems, Man and Cybernetics, volume 4, pages
4008–4014, 2003.
[162] R. Coulom. Feedforward Neural Networks in Reinforcement Learning Applied
to High-Dimensional Motor Control. In N. Cesa-Bianchi et. al.,editor,Lecture
Notes in Artificial Intelligence, pages 403–413, Berlin Heidelberg, 2002. Springer-
Verlag.
[163] G.S. Cowan and R.G. Reynolds. Learning to Access the Quality of Genetic
Programs using Cultural Algorithms. In Proceedings of the IEEE Congress on
Evolutionary Computation, volume 3, pages 1679–1686, 1999.
[164] I.L. L´opez Cruz, L.G. van Willigenburg, and G. van Straten. Efficient Differential
Evolution algorithms for multimodal optimal control problems. Applied Soft
Computing, 3(2):97–122, 2003.
[165] I.L. L´opez Cruz, L.G. van Willigenburg, and G. van Straten. Optimal Control of
Nitrate in Lettuce by a Hybrid Approach: Differential Evolution and Adjustable
Control Weight Gradient Algorithms. Computers and Electronics in Agriculture,
40(1-3):179–197, 2003.
[166] Y. Le Cun, J.S. Denker, and S.A. Solla. Optimal Brain Damage. In D. Touretzky,
editor, Advances in Neural Information Processing Systems, volume 2, pages
598–605, 1990.