Engelbrecht Andries P. Computational Intelligence: An Introduction
Подождите немного. Документ загружается.
538 REFERENCES
[787] A. Shmygelska and H.H. Hoos. An Improved Ant Colony Optimisation Algo-
rithm for the 2D HP Protein Folding Problem. In Proceedings of the Canadian
Conference on Artificial Intelligence, pages 400–407, 2004.
[788] A. Sierra and A. Echeverr´ıa. The Polar Evolution Strategy. In Proceedings of
the IEEE Congress on Evolutionary Computation, pages 2301–2306, 2006.
[789] J. Sietsma and R.J.F. Dow. Creating Artificial Neural Networks that Generalize.
Neural Networks, 4:67–79, 1991.
[790] A. Silva, A. Neves, and E. Costa. An Empirical Comparison of Particle Swarm
and Predator Prey Optimisation. In Proceedings of the Thirteenth Irish Confer-
ence on Artificial Intelligence and Cognitive Science, Lecture Notes in Artificial
Intelligence, volume 2464, pages 103–110. Springer-Verlag, 2002.
[791] K.M. Sim and W.H. Sun. Multiple Ant-Colony Optimization for Network Rout-
ing. In Proceedings of the First International Symposium on Cyber Worlds,
pages 277–281, 2002.
[792] K. Sims. Artificial Evolution for Computer Graphics. Computer Graphics,
25(4):319–328, 1991.
[793] K. Sims. Evolving Virtual Creatures. In Computer Graphics, Annual Conference
Series, ACM SIGGRAPH, pages 15–22, 1994.
[794] N. Sinha and B. Purkayastha. PSO Embedded Evolutionary Programming Tech-
nique for Nonconvex economic Load Dispatch. In Proceedings of the IEEE Power
Systems Conference and Exposition, volume 1, pages 66–71, 2004.
[795] N. Sinha, R. Shakrabarti, and P.K. Chattopadhyay. Fast Evolutionary Program-
ming Techniques for Short-Term Hydrothermal Scheduling. IEEE Transactions
on Power Systems, 18(1):214–220, 2003.
[796] P. Slade and T.D. Gedeon. Bimodal Distribution Removal. In J. Mira,
J. Cabestany, and A. Prieto, editors, Proceedings of the International Workshop
on Artificial Neural Networks, pages 249–254, Berlin, 1993. Springer-Verlag.
[797] J. Smith and T.C. Fogarty. Self Adaptation of Mutation Rates in A Steady
State Genetic Algorithm. In Proceedings of the IEEE International Conference
on Evolutionary Computation, pages 318–323, 1996.
[798] J. Smith and F. Vavak. Replacement Strategies in Steady State Genetic Al-
gorithms: Static Environments. Technical report, Intelligent Computer System
Centre, University of the West of England, Bristol, England, 1998.
[799] J.A. Snyman. A New and Dynamic Method for Unconstrained Minimization.
Applied Mathematical Modelling, 6:449–462, 1882.
[800] J.A. Snyman. An Improved Version of the Original LeapFrog Dynamic Method
for Unconstrained Minimization: LFOP1(b). Applied Mathematical Modelling,
7:216–218, 1983.
[801] K. Socha. The Influence of Run-Time Limits on Choosing Ant System Parame-
ters. In Proceedings of GECCO 2003 -- Genetic and Evolutionary Computation
Conference, volume 2723, pages 49–60, 2003.
[802] F.J. Solis and R.J.-B. Wets. Minimization by Random Search Techniques. Math-
ematical Operations Research, 6:19–30, 1981.
[803] A. Somayaji and S. Forrest. Automated Response Using System-Call Delays. In
Proceedings of the Nineth Unisex Security Symposium, pages 185–197, 2000.
REFERENCES 539
[804] A. Somayaji, S. Hofmeyr, and S. Forrest. Principles of a Computer Immune
System. In Proceedings of the ACM New Security Paradigms Workshop, pages
75–82, 1997.
[805] T. Sousa, A. Neves, and A. Silva. Swarm Optimisation as a New Tool for Data
Mining. In Proceedings of the Parallel and Distributed Processing Symposium,
pages 144–149, 2003.
[806] J.C. Spall. Introduction to Stochastic Search and Optimization. Wiley Inter-
Science, 2003.
[807] N. Srinivas and K. Deb. Multiobjective Optimization using Nondominated Sort-
ing in Genetic Algorithms. Evolutionary Computation, 2(3):221–248, 1991.
[808] A. Stacey, M. Jancic, and I. Grundy. Particle Swarm Optimization with Mu-
tation. In Proceedings of the IEEE Congress on Evolutionary Computation,
volume 2, pages 1425–1430, 2003.
[809] J.M. Steppe, K.W. Bauer, and S.K. Rogers. Integrated Feature and Architecture
Selection. IEEE Transactions on Neural Networks, 7(4):1007–1014, 1996.
[810] R. Storn. Differential Evolution Design of an IIR-Filter. In Proceedings of the
IEEE International Conference on Evolutionary Computation, pages 268–273,
1996.
[811] R. Storn. On the Usage of Differential Evolution for Function Optimization. In
Proceedings of the Biennial Conference of the North American Fuzzy Informa-
tion Processing Society, pages 519–523, 1996.
[812] R. Storn. System Design by Constraint Adaptation and Differential Evolution.
IEEE Transactions on Evolutionary Computation, 3(1):22–34, 1999.
[813] R. Storn and K. Price. Differential Evolution – A Simple and Efficient Heuristic
for global Optimization over Continuous Spaces. Journal of Global Optimization,
11(4):431–359, 1997.
[814] T. St¨utzle. Parallelization Strategies for Ant Colony Optimization. In A.E.
Eiben, T. B¨ack, M. Schoenauer, and H-P. Schwefel, editors, Proceedings of the
Parallel Problem Solving from Nature Conference, Lecture Notes in Computer
Science, volume 1498, pages 722–731. Springer-Verlag, 1998.
[815] T. St¨utzle and H. Hoos. MAX-MIN Ant System and Local Search for The Trav-
eling Salesman Problem. In Proceedings of the IEEE International Conference
on Evolutionary Computation, pages 309–314, 1997.
[816] T. St¨utzle and H.H. Hoos. MAX-MIN Ant System. Future Generation Computer
Systems, 16(8):889–914, 2000.
[817] C-T. Su and C-S. Lee. Network Reconfiguration of Distribution Systems using
Improved Mixed-Integer Hybrid Differential Evolution. IEEE Transactions on
Power Delivery, 18(3):1022–1027, 2002.
[818] M.C. Su, T.A. Liu, and H.T. Chang. An Efficient Initialization Scheme for the
Self-Organizing Feature Map Algorithm. In Proceedings of the IEEE Interna-
tional Joint Conference in Neural Networks, volume 3, pages 1906–1910, 1999.
[819] G.A. Suer. Evolutionary Programming for Designing Manufacturing Cells. In
Proceedings of the IEEE International Conference on Evolutionary Computa-
tion, pages 379–384, 1997.
540 REFERENCES
[820] P.N. Suganthan. Particle Swarm Optimiser with Neighborhood Operator. In
Proceedings of the IEEE Congress on Evolutionary Computation, pages 1958–
1962, 1999.
[821] R. Sun, S. Tatsumo, and Z. Gang. Multiagent Reinforcement Learning Method
with an Improved Ant Colony System. In Proceedings of the IEEE International
Conference on Systems, Man, and Cybernetics, volume 3, pages 1612–1617, 2001.
[822] K.K. Sung and P. Niyogi. A Formulation for Active Learning with Applications
to Object Detection. Technical Report 1438, Artificial Intelligence Laboratory,
Massachusetts Institute of Technology, 1996.
[823] R.S. Sutton. Temporal Credit Assignment in Reinforcement Learning.PhD
thesis, University of Massachusetts, Amherst, 1984.
[824] R.S. Sutton. Learning to Predict by the Method of Temporal Differences. Ma-
chine Learning, 3(1):9–44, 1988.
[825] R.S. Sutton. Implementation Details of the TD(λ) Procedure for the Case of
Vector Predictions and Backpropagation. Technical Report TN87-509.1, GTE
Laboratories, 1989.
[826] W. Sverdlik, R.G. Reynolds, and E. Zannoni. HYBAL: A Self-Tuning Algorithm
for Concept Learning in Highly Autonomous Systems. In Proceedings of the
Third Annual Conference on Artificial Intelligence, Simulation, and Planning in
High Autonomy Systems, pages 15–22, 1992.
[827] A.K. Swain and A.S. Morris. A Novel Hybrid Evolutionary Programming
Method for Function Optimization. In Proceedings of the IEEE Congress on
Evolutionary Computation, volume 1, pages 699–705, 2000.
[828] G. Syswerda. Uniform Crossover in Genetic Algorithms. In Proceedings of the
Third International Conference on Genetic Algorithms, pages 2–9, 1989.
[829] G. Syswerda. Schedule Optimization using Genetic Algorithms. In L. Davis,
editor, Handbook of Genetic Algorithms, pages 331–349. International Thomson
Computer Press, 1991.
[830] M-J. Tahk and B-C. Sun. Coevolutionary Augmented Lagrangian Methods for
Constrained Optimization. IEEE Transactions on Evolutionary Computation,
4(2):114–124, 2000.
[831]
´
E.D. Taillard. FANT: Fast Ant System. Technical Report IDSIA 46-98, IDSIA,
Lugano, Switzerland, 1998.
[832]
´
E.D. Taillard and L.M. Gambardella. Adaptive Memories for the Quadratic As-
signment Problem. Technical Report IDSIA-87-97, IDSIA, Lugano, Switzerland,
1997.
[833] T. Takagi and M. Sugeno. Fuzzy Identification of Systems and its Application to
Modeling and Control. IEEE Transactions on Systems, Man, and Cybernetics,
15(1):116–132, 1985.
[834] T. Takahashi. Principal Component Analysis is a Group Action of SO(N) which
Minimizes an Entropy Function. In Proceedings of the International Joint Con-
ference on Neural Networks, volume 1, pages 355–358, 1993.
[835] E-G. Talbi, O. Rouz, C. Fonlupt, and D. Robillard. Parallel Ant Colonies
for the quadratic assignment problem. Future Generation Computer Systems,
14(4):441–449, 2001.
REFERENCES 541
[836] H. Talbi and M. Batouche. Hybrid Particle Swarm with Differential Evolution
for Multimodal Image Registration. In Proceedings of the IEEE International
Conference on Industrial Technology, volume 3, pages 1567–1573, 2004.
[837] S. Tamura, M. Tateishi, M. Matumoto, and S. Akita. Determination of the
Number of Redundant Hidden Units in a Three-layer Feed-Forward Neural Net-
work. In Proceedings of the International Joint Conference on Neural Networks,
volume 1, pages 335–338, 1993.
[838] V. Tandon, H. El-Mounayri, and H. Kishawy. NC End Milling Optimization
using Evolutionary Computation. International Journal of Machine Tools and
Manufacture, 42(5):595–605, 2002.
[839] O. Tezak, D. Dolinar, and M. Milanovic. Snubber Design Approach for dc-dc
Converter Based on Differential Evolution Method. In Proceedings of the Eight
IEEE International Workshop on Advanced Motion Control, pages 87–91, 2004.
[840] G. Th´eraulaz and E. Bonabeau. A Brief History of Stigmergy. Artificial Life,
5:97–116, 1999.
[841] G. Th´eraulaz, E. Bonabeau, and J-L. Deneubourg. Response Threshold Rein-
forcement and Division of Labour in Insect Societies. Proceedings of the Royal
Society of London, Series B, 265:327–332, 1998.
[842] G. Th´eraulaz, S. Goss, J. Gervet, and J-L. Deneubourg. Task Differentation in
Polists Wasp Colonies: A Model for Self-Organizing of Robots. In J-A. Meyer
and S.W. Wilson, editors, Proceedings of the First International Conference on
Simulation of Adaptive Behavior: From Animals to Animats, pages 346–355.
MIT Press, 1991.
[843] H. Thiele. Fuzzy Rough Sets versus Rough Fuzzy Sets – An Interpretation and
Comparative Study using Concepts of Modal Logics. Technical Report CI-30/98,
University of Dortmund, 1998.
[844] H.H. Thodberg. Improving Generalization of Neural Networks through Pruning.
International Journal of Neural Systems, 1(4):317–326, 1991.
[845] J. Timmis. Artificial Immune Systems: A Novel Data Analysis Technique In-
spired by the Immune Network Theory. PhD thesis, University of Wales, Aberys-
twyth, August 2000.
[846] J. Timmis and M. Neal. A Resource Limited Artificial Immune System for Data
Analysis. In Research and Development in Intelligent Systems, volume 14, pages
19–32, Cambridge, UK, 2000. Springer.
[847] J. Timmis, M. Neal, and J. Hunt. Data Analysis using Artificial Immune Sys-
tems, Cluster Analysis and Kohonen Networks: Some Comparisons. In Pro-
ceedings of IEEE International Conference on Systems, Man and Cybernetics,
volume 3, pages 922–927, 1999.
[848] F. Tin-Loi and N.S. Que. Identification of Cohesive Crack Fracture Parameters
by Evolutionary Search. Computer Methods in Applied Mechanics and Engi-
neering, 191(49-50):5741–5760, 2002.
[849] S.J.P. Todd and W. Latham. Mutator, A Subjective Human Interface for Evolu-
tion of Computer Sculptures. Technical report, IBM United Kingdom Scientific
Centre Report 248, 1991.
[850] J.F.A. Traniello and R.B. Rosengaus. Ecology, Evolution and Division of Labour
in Social Insects. Animal Behaviour, 53:209–213, 1997.
542 REFERENCES
[851] I.C. Trelea. The Particle Swarm Optimization Algorithm: Convergence Analysis
and Parameter Selection. Information Processing Letters, 85(6):317–325, 2003.
[852] C-F. Tsai, C-W. Tsai, and C-C. Tseng. A Novel and Efficient Ant-Based Al-
gorithm for Solving Traveling Salesman Problem. In Proceedings of the IEEE
International Conference on Systems, Man and Cybernetics, volume 2, pages
320–325, 2002.
[853] C-F. Tsai, C-W. Tsai, H-C. Wu, and T. Yang. ACODF: A Novel Data Clustering
Approach for Data Mining in Large Databases. Journal of Systems and Software,
73:133–145, 2004.
[854] D. Tsou and C. MacNish. Adaptive Particle Swarm Optimisation for High-
Dimensional Highly Convex Search Spaces. In Proceedings of the IEEE Congress
on Evolutionary Computation, volume 2, pages 783–789, 2003.
[855] Y. Tsujimura, Y. Mafune, and M. Gen. Introducing Co-Evolution and Sub-
Evolution Processes into Genetic Algorithm for Job-Shop Scheduling. In Pro-
ceedings of the Twenty-Sixth Annual Conference of the IEEE Industrial Elec-
tronics Society, volume 4, pages 2827–2830, 2000.
[856] T. Tsukada, T. Tamura, S. Kitagawa, and Y. Fukuyama. Optimal Operational
Planning for Cogeneration System using Particle Swarm Optimization. In Pro-
ceedings of the IEEE Swarm Intelligence Symposium, pages 138–143, 2003.
[857] S. Tsutsui and D.E. Goldberg. Simplex Crossover and Linkage Identification:
Single-Stage Evolution vs. Multi-Stage Evolution. In Proceedings of the IEEE
Congress on Evolutionary Computation, volume 1, pages 974–979, 2002.
[858] A.M. Turing. Computing Machinery and Intelligence. Mind, 59:433–460, 1950.
[859] E. Uchibe, M. Nakamura, and M. Asada. Co-Evolution for Cooperative Be-
havior Acquisition in a Multiple Mobile Robot Environment. In Proceedings
of the IEEE/RSJ International Conference on Intelligent Robots and Systems,
volume 1, pages 425–430, 1998.
[860] H. Ulmer, F. Streichert, and A. Zell. Evolution Strategies Assisted by Gaussian
Processes with Improved Pre-Selection Criterion. In Proceedings of the IEEE
Congress on Evolutionary Computation, volume 1, pages 692–699, 2003.
[861] R.K. Ursem. Diversity-Guided Evolutionary Algorithms. In Proceedings of the
Parallel Problem Solving from Nature Conference, pages 462–471, 2002.
[862] F van den Bergh. Particle Swarm Weight Initialization in Multi-Layer Perceptron
Artificial Neural Networks. In Proceedings of the International Conference on
Artificial Intelligence, pages 42–45, 1999.
[863] F. van den Bergh. An Analysis of Particle Swarm Optimizers.PhDthesis,
Department of Computer Science, University of Pretoria, Pretoria, South Africa,
2002.
[864] F. van den Bergh and A.P. Engelbrecht. Cooperative Learning in Neural Net-
works using Particle Swarm Optimizers. South African Computer Journal, 26:84–
90, 2000.
[865] F. van den Bergh and A.P Engelbrecht. Effects of Swarm Size on Cooperative
Particle Swarm Optimisers. In Proceedings of the Genetic and Evolutionary
Computation Conference, pages 892–899, 2001.
REFERENCES 543
[866] F. van den Bergh and A.P. Engelbrecht. Training Product Unit Networks using
Cooperative Particle Swarm Optimisers. In Proceedings of the IEEE Interna-
tional Joint Conference on Neural Networks, volume 1, pages 126–131, 2001.
[867] F. van den Bergh and A.P Engelbrecht. Training Product Unit Networks using
Cooperative Particle Swarm Optimisers. In Proceedings of the IEEE Interna-
tional Joint Conference on Neural Networks, volume 1, pages 126–131, July
2001.
[868] F. van den Bergh and A.P. Engelbrecht. A New Locally Convergent Particle
Swarm Optimizer. In Proceedings of the IEEE International Conference on
Systems, Man, and Cybernetics, pages 96–101, 2002.
[869] F. van den Bergh and A.P. Engelbrecht. A Cooperative Approach to Parti-
cle Swarm Optimization. IEEE Transactions on Evolutionary C omputation,
8(3):225–239, 2004.
[870] F. van den Bergh and A.P. Engelbrecht. A Study of Particle Swarm Optimization
Particle Trajectories. Information Sciences, 176(8):937–971, 2006.
[871] G.N. Varela and M.C. Sinclair. Ant Colony Optimisation for Virtual-
Wavelength-Path Routing and Wavelength Allocation. In Proceedings of the
IEEE Congress on Evolutionary Computation, volume 3, page 1816, July 1999.
[872] F. Vavak and T.C. Fogarty. Comparison of Steady State and Generational Ge-
netic Algorithms for Use in Nonstationary Environments. In Proceedings of the
IEEE International Conference on Evolutionary Computation, pages 192–195,
1996.
[873] F. Vavak, K. Jukes, and T.C. Fogarty. Learning the Local Search Range for
Genetic Optimisation in Nonstationary Environments. In Proceedings of the
IEEE International Conference on Evolutionary Computation, pages 355–360,
1997.
[874] G. Venter and J. Sobieszczanski-Sobieski. Multidisciplinary Optimization of a
Transport Aircraft Wing using Particle Swarm Optimization. Structural and
Multidisciplinary Optimization, 26(1-2):121–131, 2003.
[875] G. Venter and J. Sobieszczanski-Sobieski. Particle Swarm Optimization. Journal
for the American Institute of Aeronautics and Astronautics, 41(8):1583–1589,
2003.
[876] J. Vesterstrøm and R. Thomsen. A Comparative Study of Differential Evolu-
tion, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical
Benchmark Problems. In Proceedings of the IEEE Congress on Evolutionary
Computation, volume 2, pages 1980–1987, 2004.
[877] J.S. Vesterstrøm and J. Riget. Particle Swarms: Extensions for Improved Local,
Multi-Modal, and Dynamic Search in Numerical Optimization. Master’s thesis,
Department of Computer Science, University of Aarhus, 2002.
[878] J.S. Vesterstrøm, J. Riget, and T. Krink. Division of Labor in Particle Swarm
Optimization. In Proceedings of the IEEE Congress on Evolutionary Computa-
tion, pages 1570–1575, 2002.
[879] H.L. Viktor, A.P. Engelbrecht, and I. Cloete. Reduction of Symbolic Rules from
Artificial Neural Networks using Sensitivity Analysis. In Proceedings of the
IEEE International Conference on Neural Networks, pages 1788–1793, 1995.
544 REFERENCES
[880] T.P. Vogl, J.K. Mangis, A.K. Rigler, W.T. Zink, and D.L. Alken. Accelerat-
ing the Convergence of the Back-Propagation Method. Biological Cybernetics,
59:257–263, 1988.
[881] M. Vogt. Combination of Radial Basis Function Neural Networks with Opti-
mized Learning Vector Quantization. In Proceedings of the IEEE International
Conference on Neural Networks, volume 3, pages 1841–1846, New York, 1993.
[882] V.P. Volny and D.M. Gordon. Genetic Basis for Queen-Worker Dimorphism in
a Social Insect. Proceedings of the National Academy of Sciences of the United
States of America, 99(9):6108–6111, 2002.
[883] J. Vondras and P. Martinek. Multi-Criterion Filter Design via Differential Evo-
lution Method for Function Minimization. In Proceedings of the First IEEE
International Conference on Circuits and Systems for Communications, pages
106–109, 2002.
[884] F-S. Wang and J-P. Chiou. Differential Evolution for Dynamic Optimization
of Differential-Algebraic Systems. In Proceedings of the IEEE International
Conference on Evolutionary Computation, pages 531–536, 1997.
[885] F-S. Wang and H-J. Jang. Parameter Estimation of a Bioreaction Model by
Hybrid Differential Evolution. In Proceedings of the I EEE Congress on Evolu-
tionary Computation, volume 1, pages 410–417, 2000.
[886] K-P. Wang, L. Huang, C-G. Zhou, and W. Pang. Particle Swarm Optimization
for Travelling Salesman Problem. In Proceedings of International Conference
on Machine Learning and Cybernetics, pages 1583–1585, 2003.
[887] L. Wang, X-P. Wang, and Q-D. Wu. Ant System Algorithm Based Rosenbrock
Function Optimization in Multi-Dimension Space. In Proceedings of the First
International Conference on Machine Learning and Cybernetics, pages 710–714,
2002.
[888] L.X. Wang and J.M. Mendel. Generating Fuzzy Rules by Learning from Exam-
ples. IEEE Transactions on Systems, Man, and Cybernetics, 22(6):1413–1426,
1992.
[889] X. Wang, H. Dong, and D. Chen. PID self-tuning control based on evolution-
ary programming. In Proceedings of the Fourth World Congress on Intelligent
Control and Automation, volume 4, pages 3132–3135, 2002.
[890] A Watkins and J Timmis. Artificial Immune Recognition System (AIRS): Revi-
sions and Refinements. In Proceedings of the First International Conference on
Artificial Immune Systems, volume 1, pages 173–181, 2002.
[891] C.J.H.C. Watkins. Learning from Delayed Rewards. PhD thesis, King’s College,
Cambridge University, U.K., 1989.
[892] D.J. Watts and S.H. Strogatz. Collective Dynamics of ‘Small-World’ Networks.
Nature, 393(6684):440–442, 1998.
[893] C. Wei, Z. He, Y. Zheng, and W. Pi. Swarm Directions Embedded in Fast Evo-
lutionary Programming. In Proceedings of the IEEE Congress on Evolutionary
Computation, volume 2, pages 1278–1283, May 2002.
[894] C. Wei, S. Yao, and Z. He. A Modified Evolutionary Programming. In Proceed-
ings of the IEEE International Conference on Evolutionary Computation, pages
135–138, 1996.
REFERENCES 545
[895] A.S. Weigend, D.E. Rumelhart, and B.A. Huberman. Generalization by Weight-
Elimination with Application to Forecasting. In R. Lippmann, J. Moody, and
D.S. Touretzky, editors, Advances in Neural Information Processing Systems,
volume 3, pages 875–882, 1991.
[896] J.Y. Wen, Q.H. Wu, L. Jiang, and S.J. Cheng. Pseudo-Gradient Based Evolu-
tionary Programming. Electronic Letters, 39(7):631–632, 2003.
[897] P.J. Werbos. Beyond Regression: New Tools for Prediction and Analysis in the
Behavioural Sciences. PhD thesis, Harvard University, Boston, USA, 1974.
[898] L.F.A. Wessels and E. Barnard. Avoiding False Local Minima by Proper Initial-
ization of Connections. IEEE Transactions on Neural Networks, 3(6):899–905,
1992.
[899] D. Wettschereck and T. Dietterich. Improving the Performance of Radial Basis
Function Networks by Learning Center Locations. In J.E. Moody, S.J. Hanson,
and R.P. Lippmann, editors, Advances in Neural Information Processing Sys-
tems, volume 4, pages 1133–1140, San Mateo, C.A., 1992. Morgan Kaufmann.
[900] C.M. White and G.G. Yen. A Hybrid Evolutionary Algorithm for TSP. In Pro-
ceedings of the IEEE Congress on Evolutionary Computation, volume 2, pages
1473–1478, 2004.
[901] D. White and P. Ligomenides. GANNet: A Genetic Algorithm for Optimizing
Topology and Weights in Neural Network Design. In J. Mira, J. Cabestany,
and A. Prieto, editors, International Workshop on Artificial Neural Networks,
Lecture Notes in Computer Science, volume 686, pages 332–327, Berlin, 1993.
Springer-Verlag.
[902] T. White and B. Pagurek. Towards Multi-Swarm Problem Solving in Networks.
In Proceedings of the International Conference on Multi-Agent Systems, pages
333–340, 1998.
[903] D. Whitley. A Review of Models for Simple Genetic Algorithms and Cellu-
lar Genetic Algorithms. In V. Rayward-Smith, editor, Applications of Modern
Heuristics Methods, pages 55–67, 1995.
[904] D. Whitley and C. Bogart. The Evolution of Connectivity: Pruning Neural
Networks using Genetic Algorithms. In Proceedings of the IEEE International
Joint Conference on Neural Networks, volume 1, pages 134–137, 1990.
[905] D. Whitley, T. Starkweather, and D. Fuquay. Scheduling Problems and the Trav-
elings Salesmen: The Genetic Edge Recombination Operator. In Proceedings
of the Third International Conference on Genetic Algorithms, pages 116–121.
Morgan Kaufmann, 1989.
[906] D. Whitley and N.-W. Yu. Modeling Simple Genetic Algorithms for Permuta-
tion Problems. In D. Whitley and M. Vose, editors, Foundations of Genetic
Algorithms, volume 3, pages 163–184, San Mateo, 1995. Morgan Kaufmann.
[907] B. Widrow. ADALINE and MADALINE – 1963, Plenary Speech. In Proceedings
of the First IEEE International Joint Conference on Neural Networks,volume1,
pages 148–158, 1987.
[908] B. Widrow and M.A. Lehr. 30 Years of Neural Networks: Perceptron, Madaline
and Backpropagation. Proceedings of the IEEE, 78:1415–1442, 1990.
546 REFERENCES
[909] S.T. Wiercho´nandU.Ku˙zelewska. Stable Clusters Formation in an Artificial
Immune System. In Proceedings of the First International Conference on Arti-
ficial Immune Systems, volume 1, pages 68–75, 2002.
[910] P.M. Williams. Bayesian Regularization and Pruning Using a Laplace Prior.
Neural Computation, 7:117–143, 1995.
[911] R.J. Williams. A Class of Gradient-Estimating Algorithms for Reinforcement
Learning in Neural Networks. In Proceedings of the IEEE First International
Conference on Neural Networks, pages 601–608, 1987.
[912] M. Wilson, C. Melhuish, and A. Sendova-Franks. Creating Annular Structures
Inspired by Ant Colony Behaviour using Minimalist Robots. In Proceedings of
the IEEE International Conference on Systems, Man and Cybernetics,volume2,
pages 53–58, October 2002.
[913] M. Wilson, C. Melhuish, and A. Sendova-Franks. Multi-Object Segregation:
Ant-Like Brood Sorting using Minimalism Robots. In Proceedings of the Seventh
International Conference on Simulation of Adaptive Behaviour, pages 369–370,
2002.
[914] A. Wloszek and P.D. Domanski. Application of the Coevolutionary System to
the Fuzzy Model Design. In IEEE International Conference on Fuzzy Systems,
volume 1, pages 391–395, 1997.
[915] M. Wodrich and C. Bilchev. Cooperative Distributed Search: The Ant’s Way.
Control Cybernetics, 26:413, 1997.
[916] K.P. Wong and J. Yuryevich. Evolutionary-Programming-Based Algorithms for
Environmentally-Constrained Economic Dispatch. IEEE Transactions on Power
Systems, 13(2):301–306, 1997.
[917] M.L. Wong and K.S. Leung. Data Mining Using Grammar Based Genetic Pro-
gramming and Applications. Springer, 2000.
[918] A. Wright. Genetic Algorithms for Real Parameter Optimization. In G.J.E.
Rawlins, editor, Foundations of Genetic Algorithms, pages 205–220, San Mateo,
C.A., 1991. Morgan Kaufmann.
[919] B. Wu and Z. Shi. A Clustering Algorithm Based on Swarm Intelligence. In
Proceedings of the International Conference on Info-tech and Info-net,volume3,
pages 58–66, 2001.
[920] B.L. Wu and X.H. Yu. Enhanced Evolutionary Programming for Function Opti-
mization. In IEEE World Congress on Computational Intelligence, Proceedings
of the IEEE Congress on Evolutionary Computation, pages 695–698, 1998.
[921] X. Xiao, E.R. Dow, R.C. Eberhart, Z. Ben Miled, and R.J. Oppelt. Gene
Clustering using Self-Organizing Maps and Particle Swarm Optimization. In
Proceedings of the Second IEEE International Workshop on High Performance
Computational Biology, page 10, 2003.
[922] X. Xie, W. Zhang, and Z. Yang. A Dissipative Particle Swarm Optimization.
In Proceedings of the IEEE Congress on Evolutionary Computation,volume2,
pages 1456–1461, 2002.
[923] X. Xie, W. Zhang, and Z. Yang. Adaptive Particle Swarm Optimization on
Individual Level. In Proceedings of the Sixth International Conference on Signal
Processing, volume 2, pages 1215–1218, 2002.
REFERENCES 547
[924] X. Xie, W. Zhang, and Z. Yang. Hybrid Particle Swarm Optimizer with Mass
Extinction. In Proceedings of the International Conference on Communication,
Circuits and Systems, volume 2, pages 1170–1173, 2002.
[925] L. Xu, T. Reinikainen, W. Ren, B.P. Wang, Z. Han, and D. Agonafer. A
simulation-based multi-objective design optimization of electronic packages un-
der thermal cycling and bending. Microelectronics Reliability, 44(12):1977–1983,
2004.
[926] X. Xu and R.D. Dony. Differential Evolution with Powell’s Direction Set Method
in Medical Image Registration. In Proceedings of the IEEE International Sym-
posium on Biomedical Imaging, volume 1, pages 732–735, 2004.
[927] Z. Xu, X. Hou, and J. Sun. Ant Algorithm-Based Task Scheduling in Grid
Computing. In Canadian Conference on Electrical and Computer Engineering,
volume 2, pages 1107–1110, May 2003.
[928] F. Xue, A.C. Sanderson, and R.J. Graves. Pareto-Based Multi-Objective Differ-
ential Evolution. In Proceedings of the IEEE Congress on Evolutionary Compu-
tation, volume 2, pages 862–869, 2003.
[929] Q. Xue, Y. Hu, and W.J. Tompkins. Analyses of the Hidden Units of Back-
Propagation Model. In Proceedings of the IEEE International Joint Conference
on Neural Networks, volume 1, pages 739–742, 1990.
[930] R. Yager. Multiple objective decision-making using fuzzy sets. International
Journal of Man-Machine Studies, 9:375–382, 1977.
[931] Y. Yang and M. Kamel. Clustering Ensemble using Swarm Intelligence. In
Proceedings of the IEEE Swarm Intelligence Symposium, pages 65–71, 2003.
[932] X. Yao, G. Lin, and Y. Liu. An Analysis of Evolutionary Algorithms Based on
Neighbourhood and Step Sizes. In P.J. Angeline, R.G. Reynolds, J.R. McDon-
nell, and R. Eberhart, editors, Proceedings of the Sixth Annual Conference on
Evolutionary Programming, pages 297–307, 1997.
[933] X. Yao and Y. Liu. Evolving Artificial Neural Networks through Evolutionary
Programming. In Proceedings of the Fifth Annual Conference on Evolutionary
Programming, pages 257–266, 1996.
[934] X. Yao and Y. Liu. Fast Evolutionary Programming. In L.J. Fogel, P.J. An-
geline, and T.B. B¨ack, editors, Proceedings of the Fifth Annual Conference on
Evolutionary Programming, pages 451–460. MIT Press, 1996.
[935] X. Yao and Y. Liu. Fast Evolution Strategies. In P.J. Angeline, R.G. Reynolds,
J.R. McDonnell, and R. Eberhart, editors, Evolutionary Programming VI, pages
151–161, Berlin, 1997. Springer.
[936] X. Yao, Y. Liu, and G. Liu. Evolutionary Programming Made Faster. IEEE
Transactions on Evolutionary Computation, 3(2):82–102, 1999.
[937] K. Yasuda, A. Ide, and N. Iwasaki. Adaptive Particle Swarm Optimization.
In Proceedings of the IEEE International Conference on Systems, Man, and
Cybernetics, volume 2, pages 1554–1559, 2003.
[938] S. Yasui. Convergence Suppression and Divergence Facilitation: Minimum and
Joint Use of Hidden Units by Multiple Outputs. Neural Networks, 10(2):353–367,
1997.