Some papers in my filing cabinet; further interesting
contributions in the areas of
are always of interest, whether in hardcopy or
electronic form. Please E-mail
hugh.cartwright@chem.ox.ac.uk
or -in the case of hardcopy reprints - send to Dr Hugh Cartwright,
Physical and Theoretical Chemistry Laboratory,
Oxford University, England OX1 3QZ.
Last update October 25, 1999.
1. Light Relief , I.Weeks, Lab Prac 41, 5, 33.
2. An Expert System for the Optimisation of Columns, Operating Conditions and Instrumentation for HPLC, P.J.Schoemakers et al, Chromatographia, 26 (1988), 37.
3. Review of C Darwin II, C.Naylor, PC Week, Jan 28, 1992.
4. New optimisation methods from physics and biology. D.G.Bounds, Nature 329, (1987), 215.
5. GAs and John Holland article, T. Durham, Computing, Jan 19, 1989.
6. Effectiveness of Heuristics and simulated annealing for the scheduling of concurrent tasks - an empirical comparison. C.Coroyer and Z.Liu, INRIA report 1379, January 1991.
7. A modified heuristic for an initial sequence in flowshop scheduling. I.Karimi and H-M.Ku, Ind. Eng. Chem. Res., 1988, 27, 1654
8. Scheduling in Batch Processes. H-M.Ku et al, source unknown.
9. A general stochastic model for intermediate storage in non-continuous processes. T.O.Odi and I.A.Karimi, Chem. Eng. Sci., 45, 3533, (1990)
10. Using GAs to schedule flow shop releases. G.A.Cleveland and S.F.Smith, ICGA 89, 160
11. Scheduling in serial multi-product batch processes with finite interstage storage: A mixed integer linear
program formulation. H-M.Ku and I.A.Karimi, Ind. Eng. Chem. Res., 1988, 27, 1840
12. Scheduling in serial mixed-storage multiproduct processes with transfer and set-up times. D.Rajagopalan and I.Karimi. Internal report?
13. Scheduling algorithms for serial multiproduct batch processes with tardiness penalties. H-M.Ku and I.A.Karimi. Internal report?
14. Scheduling in serial multiproduct batch processes with due-date penalties. H-M.Ku and I.Karimi, Ind. Eng. Chem. Res. 1990, 29, 580
15. Completion times in serial mixed-storage multiproduct processes with transfer and set-up times. D.Rajagopalan and I.A.Karimi, Computers chem. Eng. 13, 175, 1989
16. Completion time algorithms for serial multiproduct batch processes with shared storage. H-M.Ku and I.Karimi, Computers chem. Eng. 14, 49, 1990
17. Improving batch processes. Unknown.
18. Inductive Inference over macro-molecules. L.Allison, C.S.Wallace and C.N.Yee, Monash University Computer Science TR 90/148
19. Determination of the dimensionality of spectroscopic data by submatrix analysis. H.Cartwright, J. Chemometrics, 1, 111 (1987)
20. State space reconstruction in the presence of noise. M.Casdagli et al. Los Alamos preprint LA-UR-91-1010.
21. The emergence of coupled sequences of classifiers. R.Riolo. UMichigan TR
22. Triggered rule discovery in classifier systems L.B.Booker, ICGA 89, 265.
23. A critical review of classifier systems. S.W.Wilson and D.E.Goldberg, ICGA3 1989.
24. Biological aspects of genetic algorithms, Bibliography, R.K.Belew and C.E.Taylor, ICGA91 tutorial
25. GA theory. D.E.Goldberg, Tutorial ICGA91
26. GA theory. G.E.Liepins, ICGA 91 tutorial
27. How recombination influences optimisation in population genetics. John Holland? ICGA91 tutorial.
28. A Rosetta stone for connectionism. J.D.Farmer, Los Alamos TR LA-UR-90-228.
29. Predicting chaotic time series. J.D.Farmer and J.J.Sidorowich, Phys. rev. letts. 59, 1987, 845.
30. Exploiting chaos to predict the future and reduce noise. J.D.Farmer and J.J.Sidorowich, Los Alamos TR LA-UR-88-901.
31. Optimal shadowing and noise reduction. J.D.Farmer and J.J.Sidorowich, Physica D, LA-UR-90-653.
32. Artificial Life: The coming evolution. J.D.Farmer and A.d'A.Belin, Los Alamos TR LA-UR-90-378.
33. Coevolving high-level representations. P.J.Angeline and J.B.Pollack, LAIR TR 92-pa-coevolve.
34. apGA: An adaptive parallel genetic algorithm. G.E.Liepins and S.Baluja, Oak Ridge Internal Report.
35. [Deceptiveness and GA dynamics. G.E.Liepins and M.D.Vose. Oak Ridge Internal Report.
36. Target transformation factor analysis. P.K.Hopke, Chemometrics and Intelligent Laboratory Systems, 6, (1989), 7-19.
37. Comments on "Analysis of chemical structure-biological activity relationships using clustering methods" by Peter Jurs and Richard Lawson. L.E.Gleser, Chemometrics and Intelligent Laboratory Systems, 10 (1991), 85-86
38. Discussion of the L.A.Currie paper, The limitations of models and measurements as revealed through chemometric comparison. L.J.Gleser, source unknown.
39. List of papers by Cliff Pickover.
40. Some suggestions for terminology in Genetic Algorithms. C.E.Taylor and J.R.Merriam. (Presented at ICGA 91?)
41. Parallel GAs, population genetics and combinatorial optimisation. H.Muhlenbein, Submitted to ICGA 89.
42. Protein and nucleic acid sequence database searching: a suitable case for parallel processing. A.F.W.Coulson, J.F.Collins and A.Lyall. The Computer Journal, 30, p420 (1987)
43. How GAs work: a critical look at implicit parallelism. J.J.Grefenstette and J.E.Baker. ICGA 3, 1989.
44. Genitor: A different genetic algorithm. D.Whitley and J.Kauth. Colorado State TR CS-88-101. Also in proceedings of Rocky Mountain Conference on AI 1988.
45. Find-grained parallel GAs. B.Manderick and P.Spiessens. Brussels AI MEMO 89-3
46. TCGA publication list to 1991.
47. Dynamic parameter encoding for GAs. N.N.Schraudolph and R.K.Belew. Submtd. to Machine Learning Journal.
48. Zen and the art of Genetic Algorithms. D.E.Goldberg. ICGA 89.
49. Controlling search dynamics by manipulating energy landscapes. D.S.Touretzky. Carnegie Mellon TR CMS-CS-89-113.
50. Scheduling problems and travelling salesmen: The genetic edge recombination operator. D.Whitley, T.Starkweather and D'A Fuquay. ICGA '89.
51. Biases in the crossover landscape. L.J.Eshelman, R.A.Caruana and J.D.Schaffer. Phillips doc # TN-89-021
52. Varying the probability of mutation in the genetic algorithm. T.C.Fogarty ICGA '89?
53. ASPARAGOS. A parallel GA and population genetics. M.Gorges-Schleuter. ICGA '89?
54. GAs and information accumulation during the evolution of gene regulation. M.A.Huynen and P.Hogeweg. ICGA '89 poster?
55. as 54, MS.
56. The Genitor algorithm and selection pressure: why rank-based allocation of reproductive trials is best. D.Whitley, ICGA '89.
57. A stepwise approach to machine learning. K.Hintz, 3rd IEEE international symposium on intelligent control, 1988.
58. Procedure learning using a variable-dimension solution space. K.J.Hintz, ICGA '89, pp237-242.
59. The evolution of connectivity: pruning neural networks using GAs. D.Whitley and C.Bogart. Colorado State TR CS-89-113
60. The Genitor algorithm: Using genetic recombination to optimise neural networks. D.Whitley and T.Hanson. Colorado State TR CS-89-107
61. Analogous crossover. Y.Davidor. ICGA '89.
62. Genitor: a different genetic algorithm. D.Whitley and J.Kauth. Colorado State TR CS-88-101
63. The context-array bucket-brigade algorithm: an enhanced approach to credit-apportionment in classifier systems. Dijia Huang. ICGA '89.
64. Brain gain. Computing article June 18, 1992.
65. Spiral waves in a model of the ferroin catalysed Belousov-Zhabotinsky reaction. A.B.Rovinsky. J.Phys.Chem. 90,1986, 217.
66. Strategies for mobile phase optimisation in HPLC. A.Wright. Chromatography and analysis. April 1990.
67. Experimental design for the ruggedness testing of high-performance liquid chromatography methodology. M.Mulholland, P.J.Naish and D.R.Stout. Chemometrics and Intelligent Laboratory Systems, p263 (1989?)
68. An undergraduate experiment for the measurement of phosphorescence lifetimes. T.R.Dyke and J.S.Muenter. J. Chem. Educ. 52,(1975) 251.
69. The emergence of default hierarchies in learning classifier systems. R.L.Riolo, ICGA '89.
70. The unified laboratory program for chemistry majors. D.A.Aikens et al. J. Chem. Educ. 52 (1975),233.
71. Learning and programming in classifier systems. R.K.Belew and S.Forrest, Machine learning 3, 193-223, 1988.
72. The origin of life. M.Paecht-Horowitz. Ange. Chemie, 12, May 1973 349-438.
73. A study of rule set development in a learning classifier system. R.E.Smith and M.Valenzuela-Rendon. ICGA '89 (lengthened version.)
74. Measures of goodness of fit in linear free energy relationships. W.H.Davis and W.A.Pryor, J. Chem. Educ. 53, (1976), 285.
75. The dynamics of classifiers systems: empirical results. S.Forrest and J.H.Miller ICGA '89 (lengthened version).
76. The role of metal ions in proteins and other biological molecules. E.W.Ainscough and A.M.Brodie. J. Chem. Educ. 53, (1976), 157.
77. Free energy diagrams and concentration profiles for enzyme-catalyzed reactions. I.M.Klotz, J. Chem. Educ. 53 (1976), 159.
78. The dynamical behaviour of classifier systems. S.Forrest. Submtd. to ICGA '89.
79. Emergent behaviours of classifier systems. S.Forrest and J.H.Miller. Submtd. to Proc. of emergent computation conference. Expected to appear in Physica D, 1990.
80. A GA approach to the configuration of stack filters. C-H.H.Chu ICGA '89.
81. An introduction to receptor modelling. P.K.Kopke, Chemometrics and Intelligent Laboratory Systems 10 (1991) 21-43.
82. Development of Multivariate analysis procedures for Ontario air quality data. P.K.Hopke and Y.Zeng. Proc. Technology transfer conference, Part A Air quality research.
83. The application of factor analysis to source apportionment of aerosol mass. C-K.Liu et al. Am. Ind. Hyg. Assoc J. (43), 1982, 314.
84. Investigation of the use of chemical mass balance receptor model: numerical computations. M.D.Cheng and P.K.Hopke. Chemometrics and Intelligent Laboratory Systems 1 (1986), 33-50
85. Invited comments. L.J.Glesser. Chemometrics and Intelligent Laboratory Systems, p44, unknown issue.
86. Application and verification studies of TTFA as an aerosol receptor model. P.K.Hopke, K.G.Severin and S-N.Chang. Speciality conference ion receptor models applied to contemporary pollution problems, 1982.
87. Measurement error models. L.J.Glesser. Chemometrics and Intelligent Laboratory Systems, 10 (1991) 45-57.
88. Inert (concerning fluorocarbons in atmosphere) source unknown.
89. 1+1=2 notice.
90. Analysis of drugs in body fluids. R.N.Gupta and D.Lewis. Chemistry in Canada, June 1975, 30.
91. Forensic analysis. A.D.Beveridge. Chemistry in Canada, June 1975, 38.
92. On being sane in insane places. D.L.Rosenhan. Science, 250, (1973).
93. Weighting factors in least squares. D.E.Sands, J. Chem. Educ. 51, (1974) p473.
94. Linear least-squares analysis. S.D.Christian, E.H.Lane and F.Garland, J. Chem. Educ., 51 (1974), 475.
95. Sensitivity and limit of detection in quantitative spectrometric methods. J.D.Ingle, J. Chem. Educ., 51 (1974) p100.
96. Zwitterions. source unknown.
97. Activities from vapour-pressure data. source unknown.
98. Photohydrolysis of monochloroacetic acid (quantum efficiency) source unknown.
99. Standard buffer solutions, source unknown.
100. Checking of spectrometers, source unknown.
101. The use of constrained least-squares to solve the chemical mass balance problem. D.Wand and P.K.Hopke, Atmospheric Environment 10 (1989) 2143.
102. A chemistry projects laboratory. J.I.Steinfeld. J. Chem. Educ. 46 (1969), 232.
103. The kinetics of electrode processes. K.J.Laidler, J. Chem. Educ. 47 (1970), 600.
104. Identification of markers for chemical mass balance receptor model. M-D.Cheng and P.K.Hopke, Atmospheric Environment 23 (1989) 1373.
105. Comparison of the source locations and their seasonal patterns for sulphur species in precipitation and ambient particles in Ontario, Canada. Proceedings of the 1990 EPA/A & WMA international symposium. Y.Zeng and P.K.Hopke.
106. Empirical relation between sulphur dioxide emissions and acid deposition from monthly data. C.B.Epstein and M.Oppenheimer. Nature, 323,(1986), 245.
107. Comparison of particles taken from the esp and plume of a coal-fired power plant with background aerosol particles. D.S.Kim et al, Atmospheric Environment 23 (1989), 81.
108. Photo-oxidation of leuco methyl crystal violet. F.C.Thyrion, J. Chem. Educ., 48, (1971), 766.
109. Genetic algorithms and their applications. J.J.Grefenstette, AIC-90-006 (in The encyclopaedia of computer science and technology).
110. Strategy acquisition with GAs. J.J.Grefenstette, AIC-90-007 (in the Handbook of GAs).
111. Competition-based learning for reactive systems. J.J.Grefenstette. AIC-90-008. 1990 DARPA workshop.
112. Conditions for implicit parallelism. J.J.Grefenstette. AIC-91-012. (in Foundation of GAs).
113. Learning sequential decision rules using simulation models and competition. J.J.Grefenstette, C.L.Ramsey and A.C.Schultz. AIC-90-010 (in Machine Learning 5, 1990, 355).
114. Simulation-assisted learning by competition: effects of noise differences between training model and target environment. C.L.Ramsey, A.C.Schultz and J.J.Grefenstette. AIC-90-011 (in 1990 machine learning conference.)
115. Improving tactical plans with GAs. A.C.Schultz and J.J.Grefenstette. AIC-90-012 (in IEEE conference TAI 1990).
116. Using NNs and GAs as heuristic for NP-complete problems. W.M.Spears and K.A.Dejong. AIC-90-013 (in NN conference 1990).
117. An analysis of multi-point crossover. W.M.Spears and K.A.Dejong. AIC-91-021 (in Foundations of Gas workshop.)
118. AI technical paper abstracts 1991 (US Naval Research Lab.)
119. An investigation into the use of hypermutation as an adaptive operator in GAs having continuous, time-dependent non-stationary environment. H.G.Cobb. AIC-90-001
120. GA-based learning. K.A.Dejong. AIC-90-002. (in Machine learning vol III)
121. An analysis of the interacting roles of population size and crossover in GAs. K.A.Dejong and W.M.Spears.
AIC-90-003 (1st conference on parallel problem solving from nature, 1990)
122. Active bias adjustment for incremental supervised concept learning. D.F.Gordon. PhD thesis, University of Maryland UMIACS-TR-90-60.
123. Explanations of empirically derived reactive plans. D.F.Gordon and J.J.Grefenstette. AIC-90-005 (7th machine learning conference).
124. New Scientist report on dispersal GA.
125. Biological aspects of GAs. R.K.Belew. Notes from ICGA 91 tutorial.
126. Spontaneous emergence of a metabolism. R.J.Bagley and J.D.Farmer. Los Alamos TR LA-UR-91-1707, submtd to Artificial Life.
127. R.J.Bagley, J.D.Farmer and W.Fontana. Los Alamos TR? Artificial Life II in the Sciences of Complexity, 1991.
128. Non-linear modelling and prediction by successive approximation using radial basis functions. X.He and A.Lapedes. Los Alamos TR LA-UR-91-1375. submtd to Physica D.
129. A mutli-valued logic predicate calculus approach to synthesis planning. W.T.Wipke and D.P.Dolata. in AI applications in chemistry.
130. Genetically generated neural networks I: representational effects. L.Marti
131. Genetically generated neural networks II: searching for an optimal representation. L.Marti.
132. Static analysis to identify vectorizable numerical domain in logic programs for efficient execution of scientific expert systems. A.K.Bansal and D.S.Poduval. Kent State TR CS-9101-05
133. On the optimal stochastic scheduling of out-forests. E.G.Coffman and Z.Liu. Unite de recherche inria-sophia antipolis TR 1156.
134. Optimal ergodic control of non-linear stochastic systems. F.Campillo. INRIA TR 1257.
135. Annealing, Image analysis Mean field annealing using compound Gauss-Markov random fields for edge detection and image restoration. J.Zerubia and R.Chellappa. INRIA TR 1295.
136. Optimum spectrophotometer parameters. Cary AR 14-2.
137. Noise in double-beam uv-vis spectrophotometers. Perkin-Elmer applications data bulletin.
138. Maintaining optimum spectrophotometer performance. Perkin-Elmer applications data bulletin.
139. Filters. Reprint from the Optical Industry and Systems Purchasing Directory and Encyclopaedia.
140. On-line continuous potentiometric measurement of potassium concentration in whole blood during open-heart surgery. H.F.Oswald et al. Clin. Chem. 25/1, 39-43 (1979)
141. Thermal analysis of electronic materials. J.V.Wood. Du Pont publication.
142. Increased flexibility in GAs: the use of variable Boltzmann selective pressure to control propagation. M.De La Maza and B.Tidor in Computer Science and Operations Research, Pergammon Press, p.425.
143. Use of Expert System shells in the design of ACExpert: Automated AA spectrometry. W.R.Browett and M.J.Stillman, Prog. analyt. Spectrosc., 12, 73, 1989.
144. ACexpert. Design and Implementation of AXselect, AAexpert and GC-MSexpert: Expert systems that aid in the analysis of environmental samples. M.J.Stillman et al in World Congress on Expert Systems, December 1991.
145. Acexpert. Automated metal analysis by atomic absorption. W.R.Browett, T.A.Cox and M.J.Stillman. ACS Symposium series 408, 1989, pp210-235.
146. Optimisation of parameters for semi-empirical methods, Parts I and II J.J.P.Stewart, J. Comp. Chem. 10, 209 (1989)
147. Messy GAs: motivation, analysis and first results. D.E.Goldberg, B.Korb and K.Deb. TCGA report 89003
148. Explorations of the mean field theory learning algorithm. C.Peterson and E.Hartman. MCC technical report ACA-ST/HI-065-88
149. Optimising small neural networks using a distributed GA. D.Whitley and T.Starkweather. Colorado State TR CS-89-114
150. Optimisation of Steiner trees using GAs. J.Hesser, R.Manner and O.Stucky. Source unknown, but possible ICGA 89.
151. Errors in Calibration graphs. J.N.Miller. Spectroscopy International, 3, 47.
152. The principles of principal component analysis. T.Davies. Spectroscopy Europe 4, 38, (1992)
153. Smart materials: creating systems that react. T.Studt, R & D magazine, April 1992, 55.
154. General asymmetric neural networks and structure design by genetic algorithms. S.Bornholdt and D.Graudenz. Neural Networks, 5, 327 (1992)
155. Messy genetic algorithms: motivation, analysis and first results. D.E.Goldberg, B.Korb and K.Deb. TCGA report 89003.
156. Separation system synthesis: a knowledge-based approach. 2 gas/vapour mixtures. S.D.Barnicki and J.R.Fair. Ind. Eng. Chem. Res. 31, 1679 (1992)
157. On the nature of mesophase transitions in polymers I. The isotropic-nematic transition. W.Pechold,
H.P.Grossman and E.Sautter. Colloid Polym Sci, 270:639 (1992)
158. Distillation column sequencing using marginal price. A.K.Modi and A.W.Westerberg. Ind. Eng. Chem. Res., 31, 839 (1992)
159. Development of the cluster-size distribution in flowing suspensions. R.D.Cohen, AIChE Journal, 38, 1129 (1992)
160. Rule-based system for the synthesis of heat exchanger networks. J.A.Souto and J.J.Casares, Expert Systems with Applications, 5, 111 (1992)
161. Statistical approach to the solution of first-kind integral equations arising in the study of materials and their properties. J.D.Wilson, J. Materials Sci 27, 3911 (1992)
162. Application of a Bayesian regression method to the estimation of diffusivity in hydrophilic gels. W.A.Anderson et al. Can. J. Chem. Eng., 70, 499 (1992)
163. Applications of Genetic Algorithms in chemometrics. C.B.Lucasius and G.Kateman, Intl Conf. on GAs 1989?
164. Micelles and aggregates of fluorinated surfactants. W.Guo, T.A.Brown and B.M.Fung. J. Phys. Chem 95, 1829 (1991)
165. C pattern-matching algorithms. A.Dillon, Computing 20 August 1992, p24.
166. Construction of high-order deceptive functions using low-order Walsh coefficients. D.E.Goldberg. Illigal report 90002, December 1990.
167. A Laboratory course for students in science-related fields. K.W.Morse, J. Chem.Educ., 53, 317 (1976).
168. Simple fluorimetric analysis of glycine in dietetic beverages. E.D.Coppola and J.G.Hanna. J.Chem.Educ., 53, 322, (1976)
169. Student preparation and analysis of chloride and calcium ion selective electrodes. B.W.Lloyd, F.L.O'Brien and W.D.Wilson. J. Chem. Educ., 53, 329 (1976)
170. Resources in environmental chemistry. J.W.Moore and E.A.Moore. J. Chem. Educ., 53, 167 (1976)
171. An advanced laboratory experiment in bioinorganic chemistry. B.A.Bechman et al. J.Chem.Educ., 53, 387 (1976)
172. The simultaneous potentiometric titration of Cu and Fe in non-aqueous media. R.D.Braun, J.Chem.Educ., 53, 463 (1976)
173. An undergraduate electroanalytical experiment. J.Janata. J.Chem.Educ., 53, 399 (1976)
174. Training feed-forward neural networks using genetic algorithms. David Montana and Lawrence Davis.
175. Generalising the notion of schema in genetic algorithms. Michael Vose.
176. Modelling genetic algorithms with Markov chains. Allen Nix and Michael Vose.
177. Why, having so may neurons, do we have so few thoughts? James Anderson, Relating theory and data.
178. Snob: a program for discriminating between classes. J.D.Patrick.
179. ICGA91 participants list (includes e-mail addresses).
180. Learning and prediction of nuclear stability by NNs. S.Gazula, J.W.Clark ad H.Bohr.
181. Learning and bucket brigade dynamics in classifier systems. M.Compiani, D.Montanari and R.Serra.
182. VCS: variable classifier systems. Lingyan Shu and Jonathan Schaeffer.
183. Parallel architectures and NNs. E.R.Caianello.
184. A hybrid GA for the estimation of kinetic parameters. Brynn Hibbert, Chemometrics and Intelligent Lab Systems 19 (1993) 319-329.
185. GAs in chemistry Brynn Hibbert, Chemometrics and Intelligent Lab Systems, 19 (1993) 277-293.
186. Generation and display of chemical structures by GAs, Brynn Hibbert, Chemometrics and Intelligent Lab Systems 20 (1993) 35-43.
187. Using evolutionary programming for modelling: an ocean acoustic example. David Fogel;, IEEE Journal of Oceanic Engineering, 17, (1992), 333.
188. A specialised GA for numerical optimisation problems, Cezary Janikow and Zbignew Michaelewicz, End Int. IEEE conference on tools for AI proceedings, 1990, pp 798-804.
189. Binary and floating point function optimisation using mGAs. Kalyanmoy Deb.
190. Real-coded GAs, virtual alphabets and blocking. David Goldberg.
191. Rosetta: towards a model of learning problems. Stephen Smith and Stewart Wilson, ICGA 3.
192. Induction through knowledge basenorm aliasation. G.D.Ooshhuizen and D.R. McGregor.
193. Laser interferometry as a chemical tool. R.N.O'Brien.
194. Interferometry for electrochemical investigation. R.N.O'Brien, Review of scientific instruments, 35 (1964) 803-806.
195. Applications of GAs in chemometrics. C.B.Lucasius and G.Kateman Poster at ICGA3 (?)
196. GAs and Walsh functions: Part II deception and its analysis. David Goldberg.
197. A note on the non-uniform Waalsh-schema transform, Clayton Bridges and David Goldberg.
198. GAs and Walsh functions. Part I a gentle introduction., David Goldberg.
199. Relative building-block fitness and the building-block hypothesis. Stephanie Forrest and Melanie Mitchell.
200. Odour discrimination with an electronic nose. Harold Shurmer ands Julian Gardner. Sensors and Actuators B, 8 (1992) 1-11.
201. What makes a problem hard for a GA? Some anomalous results and their explanation. Stephanie Forrest and Melanie Mitchell, Machine Learning.
202. Punctuated equilibria in genetic search. Michael Vose and Gunar Liepins.
203. Isomorphisms in GAs. David Battle and Michael Vose.
204. Representational Issues in genetic optimisation, Gunar Liepins and Michael Vose.
205. Schema disruption. Michael Vose and Gunar Liepins.
206. Time-derivative models of Pavolovian reinforcement. Richard Sutton and Andrew Barto.
207. Deterministic Boltzmann learning performs steepest descent in weight space. Geoffrey Hinton, University of Toronto Tech.. Rep. CRG-TR-89-1.
208. A bibliography of the intersection of genetic search and artificial NNs Mike Rudnick.
209. The internet worm program: an analysis. Eugene Spafford.
210. Reconstructing a temperature time series for a station in south-eastern Australia, Zhenjie Lin, Neil Plumer and Simon Torok.
211. Refining Australia's historical climate record. Simon Torok, Beth Lavery, Neil Plummer, Alex Kariko and Neville Nicholls.
212. Certainty from uncertainty. Charles Bennet, Nature 362 (1993), 694.
213 The parallel GA as function optimiser. H.Muhlenbein, M.Schomisch and J.Born, Parallel Computing, 17 (1991) 619-632.
214. The stable room-mates problem. Paul Leather.
215. Teachers and classes with NNS Lars Gislen, C.Dartsenm Peterson and Bo Soderberg.
216. Non-linear prediction of chaotic time series. Martin Casdagli.
217. A study of the practical limitations of PCA and the labelling of unresolved HPLC peaks,. P.J.Naish, J.Lynch and T.Blaffert, Chromatographia, 27 (1989) 343-358.
218. An expert system for designing an intelligent spreadsheet for evaluation of precision of liquid chromatographic methods. M.Mulholand, J.A.van Leeuwen and B.Vandeginste, Aanaltyica Chimica Acta 223 (1989) 183-192.
219. Evaluation of a complete HPLC solvent optimisation system involving piece-wise quadratic modelling.
P.Naish-Chamberlin and R.J.Lynch, Chromatographia 29 (1990 79-89.
220. Asymptotic dynamics of classifier systems. M. Companiani, D.Montanari, R.Serra and P.Simonini, ICGA 3.
221. Dynamical systems in AI: the case of classifier systems. M.Companiani, D.Montanari, R.Serra and P.Simonini, Connectionism in Perspective, 331.
222. A rational reconstruction of Wilson' animat and Holland's CS-1. Gary Roberts.
223. Experimental certification of a model describing the transient behaviour of a reaction system approaching a limit cycle or a runaway in a CSTR. D.P.Vermeulen and J.M.H.Fortuin, Chemical Eng. Sci. 41 (1986) 1089-1095.
224. Limit cycles measured in a liquid-phase reaction system. A.H.Heemskerk, W.R.Dammerds and
J.M.H.Fortuin, Chemical Eng. Sci., 35 (1980) 439-445.
225. Experimental verification of a model describing large temperature oscillations of a limit-cycle approaching liquid-phase reaction system in a CSTR. D.P.Vermeulen and J.M.H.Fortuin, Chemical Eng. Sci. 41, (1986) 1291-1302.
226. Darwinism applied to machine learning. Barry McMullin.
227. Nonlinear modelling of chaotic time series: theory and applications. Martin Casdagli et al.
228. Characterising crossover in GAs. Gunar Liepins and Michael Vose.
229. Deceptiveness and GA dynamics. Gunar Liepins and Michael Vose.
230. Evolving behaviours in the iterated prisoners' dilemma. David Fogel.
231. Using evolutionary programs to create NNs that are capable of playing tic-tac-toe. David Fogel.
232. A genetic learning system for economic control of power plants. Juan Velasco, Luis Magdalena and Antonio Rodriguez in Modelling and control of water resources systems and global changes, Ghent, November 6-8, 1991.
233. Inductive learning applied to fossil power plants control optimisation. Juan Velasco, Gregorio Fernandez and Luis Magdalena. Symposium on the control of power plants and power systems, 1992.
234. A control architecture for optimal operation with inductive learning. Luis Magdalena, Juan Velasco et al. Symposium on intelligent components and instruments for control applications, 1992.
235. Learning with fuzzy logic.: a way to combine genetic algorithms with fuzzy logic. S.Rodriguez, A.Paracio and
Juan Velasco in International fuzzy systems and intelligent control conference, 1993.
236. Implicit parallelism in genetic algorithms. A.Bertoni and M.Dorigo, Artificial Intelligence (61) 2, 307-314.
237. New representation and operators for GAs applied to grouping problems. E.Falkenauer.
238. A genetic algorithm for flowshop sequencing. Colin Reeves. Computers and OR.
239. Dialogue optimisation in the LOLITA natural language processor using evolutionary algorithms. D.J.Nettleton and R.Garigliano. Submitted for in inclusion in the proceedings of the Unicom conference, March
1994.
240. A genetic algorithm to solve the timetable problem. A.Colorni, M.Dorigo and V.Maniezzo. Under journal submission.
241. Design of a pipe network using genetic algorithms. L.J.Murphy, A.R.Simpson, and G.C.Dandy, Water, August 40-42.
242. RPL2 a parallel genetic algorithms language, interpreter and framework. Call for alpha and beta test sites.. Nick Radcliffe?
243. Pipe network optimisation using genetic algorithms. A.Simpson, L.Murphy and G.Dandy, Proc. ASCE water resources planning and management division specialty conference, Seattle, Wa.
244. A review of pipe network optimisation techniques. G.C.Dandy, A.R.Simpson and L.J.Murphy. Presented at Watercomp 93.
245. Genetic algorithms-based design and optimisation of statistical quality-control procedures. A.T.Hatimihail.
Clin. Chem. 39/9 1972-8 (1993).
246. Genetic algorithms in pipe network optimisation. L.J.Murphy and A.R.Simpson. Research report no R93, dept,. of civil engineering, University of Adelaide.
247. Genetic algorithms for images. HG.VanHove and A.Verschoren. huho@ruca.ua.ac.be
248. Genetics-based machine learning and behaviour-based robotics: a new synthesis. M.Dorigo, IEEE trans. on systems, man and cybernetics, 23, (1993) 141.
249. The effect of sensory information on reinforcement learning by a robot arm. M.Dorigo, M.Patel and M.Colombetti, Proceedings of ISRAM 94, Maui, USA. mdorigo@ulb.ac.be, patel@ipmel2.elet.polimi.it, colobet@ipmel2.elet.polimi.it
250. Robot shaping: developing situated agents through learning. M.Dorigo and M.Colombetti, to appear in AI Journal.
251. Reducing epistasis in combinatorial problems by expansive coding. D.Beasley, D.R.Bull and R.R.Martin, ICGA5 pp400-407. david.beasley@cm.cfd.ac.uk, dave.bull@bristol.ac.uk, ralph.martin@cm.cf.ac.uk
252. ? (retrieved by Andrew Tuson from Holland perhaps?)
253. An indexed bibliography of genetic algorithms: 1957-1993. J.T.Alander, Report 94-1, University of Vaasa. jal@uwasa.fi.
254. Optimizing self-organizing control architectures with genetic algorithms: the interaction between natural selection and ontogenesis. N.Almassy and P.Verschure. To appear in Proceedings of the PPSN 92.
255. A new modelling paradigm for strategic planning. L.D.Chambers and M.A.P.Taylor.
256. COLOS: conceptual learning of science. H.Haertel.
257. Distributed optimization by ant colonies. A.Colorni, M.Dorigo and V.Maniezzo, Proceedings of ECAL91, 134-142,Elsevier.
258. An investigation of some properties of an ant algorithm. A.Colorni, M.Dorigo and V.Maniezzo. Proceedings of the parallel problem solving from nature conference (PPSN 92).
259. The ant system: an autocatalytic optimizing process. M.Dorigo, V.Maniezzo and A.Coilorni, Submitted to IEEE transaction on systems, man and cybernetics.
260. Studies of continuous-flow chemical synthesis using genetic algorithms. H.M.Cartwright and J.R.Cattell. To appear in Applications of Modern Heuristics, 1995.
261. Fast practical evolutionary timetabling. D.Corne, P.Ross and H-L Fang. Presented at AISB workshop on evolutionary computing, Leeds, 1994?
262. An overview of genetic algorithms: Part I fundamentals. D.Beasley, D.R.Bull and R/.Martin, University Computing 1993, 15(2) 58-69.
263. Exploring optimal parameters for multiple fault diagnosis using the simple genetic algorithm. M.Juric,
Research report AI-1993-05, AI program, University of Georgia.
264. Genetic algorithms in chemistry. B.Hibbert, Chemometrics and Intelligent Lab. Systems, 19 (1993) 277-293.
265. An overview of Genetic algorithms. D.Beasley, D.Bull and R.Martin, University Computing 1993, 15(4) 170-181.
266. Impedance measurements of the relaxation phenomena in the bismuth/anodic film/electrolyte system. M.Bojinov, I.Kanazirski and A.Girginov, Electrochimica Acta 37 (13) (1992) 2415-2420
267. Electrical properties of the barrier layer/solution interface and its role during breakdown of anodic bismuth oxide films. I.Kanazirski, M.Bojinov and A.Girginov, Electrochimica Acta 38, pp511-517, 1993.
268. Kinetics of the anodic oxidation of bismuth in glycol-borate electrolyte - a space charge approach. I.Kanazirski, M.Bojinov and A.Girginov. Electrochimica Acta 38, pp1061-1065, 1993.
269. Electrochemical behaviour of passive tin electrode in H2SO4 solutions at very positive potentials. M.Bojinov, K.Salmi and G.Sundholm. J. Electroanal. Chem, 358(1993) 177-191.
270. The antimony/klebelsbergite electrode. M.Bojinov and D.Pavlov. J. Electroanal. Chem. 367(1994) 195-204.
271. A space charge approach to the growth and breakdown of anodic passive filsm on metals. M.Bojinov. Presented at 7th Int. Symp. on Passivity of metals. Clausthal, FRG, Aug. 94.
272. A model for the passivation of bismuth in concentrated sulphuric acid solutions, emphasizing the structure and properties of the anodic layer. M.Bojinov, T.Tzvetkoff, A.Girginov. Presented at the 7th Int. Symp. on Passivity of metals. Clausthel, FRG, August 1994.
273. Corrosion of nickel, iron, cobalt and alloys thereof in molten salt electrolytes. T.Tzvetkoff, M.Bojinov and A.Girginov. Submitted to J. Meter. Sci (13.2.94)
274. Processes at electrodeposition of refractory metals (Ti, Zr, Nb, Ta) from molten salt electrolytes. T.Tzvetkoff, M.Bojinov and A.Girginov, Subt. to J. Appl Electrochem. 3.5.94
275. Evolutionary divide and conquer (I): a novel approach to the TSP. Christine Valenzuela and Antonia Jones, Evolutionary Computing 1(4), 313-333, 1994.
276. Exploring optimal parameters for multiple fault diagnosis using the simple genetic algorithm. Mark Juric, University of Georgia report AI-1993-05
277. The implementation of a genetic algorithm for the scheduling and topology optimisation of chemical flowshops. A.L.Tuson, Internal technical report.
278. Basic concepts of multimedia didactics Ludwig Issing, Paper presented at the International workshop on Computer Aided Technology, Berlin October 6-7, 1993.
279. Constructivist perspectives on science and mathematics teaching. Grayson H. Wheatley, Science Education 75(1): 9-21 (1991).
280. Genetic algorithms and highly constrained problems: the timetable case. A Colorni, M.DORIGO, V.Maniezzo. Lecture notes in computer science, 496. Parallel problem solving from nature 1990.
281. Constrained gas network pipe sizing with genetic algorithms. Ian Boyd, Patrick Surry and Nicholas Radcliffe. Subt to Parallel problem solving from nature, 1994.
282. Genetic algorithms: a new approach to the timetable problem. A.Colorni, M.DORIGO and V.Maniezzo.
283. RPL2: a language and parallel framework for evolutionary computing. Patrick Surry and Nicholas Radcliffe. Submt to parallel problem solving from nature, 1994.
284. Genetic algorithms and very fast simulated annealing: a comparison. Lester Ingber and Bruce Rosen, Math. Comput. Modelling, 16, 87-100, 1992.
285. Equivalence class analysis of genetic algorithms., Nicholas Radcliffe. Complex systems 5 (1991) 183-205.
286. Genetic set recombination and its application to neural network topology optimisation. Nicholas Radcliffe. EPCC tech. rep. EPCC-TR-91-21.
287. Forma analysis and random respectful recombination. Nicholas Radcliffe. Proc. 4th Intl. Conference on Gas.
288. The schema theorem and Price's theorem. Lee Altenberg . To appear in Foundations of Genetic Algorithms, ed. Whitley and Vose.
289. Genetic set recombination. Nicholas Radcliffe.
290. Non-linear genetic representations. Nicholas Radcliffe.
291. The algebra of genetic algorithms. Nicholas Radcliffe, Annals of Maths and AI, 1994.
292. Evolutionary algorithms: theory and applications. Heinz Muhlenbein.
293. Parallel recombinative simulated annealing: a genetic algorithm. S.W.Mahfoud and David Goldberg. To appear in Parallel Computing.
294. Genetic programming with adaptive representations. Justian Rosca and Dana Ballard.
295. An efficient algorithm for solving the token distribution problem on k-ary d-cube networks. Claude Diderich and Marc Gengler. Tech Rept. LITH-97.
296. Co-operation through hierarchical competition in genetic data mining. Nicholas Radcliffe and Patrick Surry. Subt. to Parallel problem solving from nature.
297. Optimal use of the SCE-UA global optimisation method for calibrating watershed models. Q.Duan, S.Sorooshian and V.Gupta. J. Hydrology 158 (1994) 265-284.
298. Evolutionary algorithms and emergent intelligent (PhD thesis) Peter John Angeline, Ohio State University, 1993.
299. 1504 studies of the self-organising map (SOM) and learning vector quantisation (LVQ) (Neural networks research centre, Helsink University of Technology).
300. Facility management of distribution centres for vegetables and fruits (Abstract only) R.A.C.M.Broekmeulen. (Submtd to AISB 95).
301. The use of local search suggestion lists for improving the solution of timetable problems with evolutionary algorithms. Ben Paechter, Andrew Cumming and Henri Luchain (extended abstract submtd to AISB 95)
302. Evolutionary learning in computational ecologies: an application to adaptive distributed routing in communication networks. Brian Carse, Terry Fogarty and Alistair Munro (extended abstract submtd to AISB 95).
303. Rapid and quantitative analysis of bioprocesses using pyrolysis mass spectrometry and neural networks: application to indole production, Royston Goodacre and Douglas Kell, Analytica Chimica Acta 279 (1993) 17-26.
304. Rapid identification using pyrolysis mass spectrometry and artificial neural networks of Propionibacterium acnes isolated from dogs. R. Goodacre et al. J. Appl. Bacteriology 1994, 76, 124-134.
305. Characterization and quantification of microbial systems using pyrolysis mass spectrometry: introducing neural networks to analytical pyrolysis. Royston Goodacre. Microbiology Europe 2(2) 16-22, 1994.
306. Rapid and quantitative analysis of the pyrolysis mass spectra of complex binary and tertiary mixtures using multivariate calibration and artificial neural networks. Royston Goodacre, Mark Neal and Douglas Kell. Analytical chemistry 1984, 66, 1070-1085.
307. A genetic algorithm for the set covering problem. J.E.Beasley and P.C.Chu, presented at the Applied Decision Technologies Conference, Brunel University, London, April 3-5, 1995.
308.INNE, interactive neural network environment: user manual, M.A.Alberti, P.Marelli, S.Trapella, March 1993.
309. Automata and neuralworks: learning environments for automata theory and neural nets. M.A.Alberti, P.Marelli and N.Sabadini in Rethinking the roles of technology in education.
310. A neural algorithm for the maximum satisfiability problem. M.A.Alberti, P.Marelli and R.Posenato.
311. Evolutionary learning in computational ecologies: an application to adaptive distributed routing in communication networks. Brian Carse, Terry Fogarty and Alistair Munro. Presented at AISB 1995.
312. The use of local search suggestion lists for improving the solution of timetabling problems with evolutionary algorithms. Ben Paechter, Andrew Cumming and Henri Luchian. Presented at AISB 1995.
313. The ant colony metaphor for searching continuous design spaces. G.Bilchev and I.C.Parmee. Presented at AISB 1995.
314. An evolutionary algorithm for parametric array signal processing./ Dekun Yang and Stuart Flockton. Presented at AISB 1995.
315. Genetic algorithms for the placement problem. A.J.Swann. Presented at AISB 1995.
316. Radial basis Boltzmann machines and learning with missing values. Hilbert J.Kappen and Marcel J.Nijman, submitted to World congress on neural networks, 1995.
317. Unsupervised filtering of color spectra. Reiner Lenz et al
318. Applications of evolutionary computing to behavioural ecology. Paul Devine, Ray Paton and Geoff Parker.
319. Apply case-based reasoning to manufacturing. David Hinkle and Christopher Toomey, AAAI, Spring 1995, 65
320. Improving human decision making through case-based decision aiding, Janet Kolodner, AI Magazine, Summer 1991, 52
321. Case-based reasoning: a research paradigm. Stephen Slade, AI Magazine, Spring 1991, 42.
322. Case adaptation in autoclave layout design. Ralph Barletta and Dan Hennessy.
323. Applying case-based reasoning to autoclave loading. Daniel Hennessy and David Hinkle. IEEE Expert, October 1992, 21.
324. Using case-based retrieval for customer technical support. Evangelos Simoudis, IEEE Expert, October 1992, 7
325. Genetic based machine learning for event processing in artificial vision. Geoff Kendall, Ray Paton and Dave Jackson
326. Some combinatorial landscapes on which a genetic algorithm outperforms other stochastic iterative methods. Dave Corne and Peter Ross
327. Genetic algorithm optimization of multi-peak problems: studies in convergence and robustness. A.J.Keane, Artificial Intelligence in Engineering 9 (1995) 75-83.
328. A multi-objective approach to constrained optimisation of gas supply networks. Patrick Surry, Nicholas Radcliffe and Ian Boyd,
329. Exploring some commercial applications of genetic programming, Gerald Robinson and Paul McIlroy
330. Genetic MasterMind, a case of dynamic constraint optimization, J.J.Merelo
331. Information transmission in genetic algorithm and Shannon's second theorem. Hillol Kargupta.
332. Supervised learning in neural networks without explicit error back-propagation. Robert Brandt and Feng Lin. 32nd annual Allerton conference on communication, control and computing, September 28-30, 1994, pp 294-303.
333. Distributed autonomous hierarchical hypergraph operations for problem solving., M.Kubo and Y.Kakazu, abstract for IEEE 95.
334. On mean convergence time for simple Gas. T.Niwa and M.Tanaka, submitted to IEEE 95.
335. Lamarkian GA with genetic supervision. S.Yoshii K.Suzuki and Y.Kakazu, abstract for IEEE 95.
336. Machine requirements planing and workload assignment using genetic algorithms. B.Porter, K.L.Mak and Y.S.Wong, abstract for IEEE 95.
337. GA in lift group control optimisation J.T.Alander, J.Herajarvi, J.Ylinen and T.Tyni, abstract for IEEE 95.
338. Genetic algorithm with neutral mutations for massively multimodal function optimisation. K.Okhura and K.Ueda, abstract for IEEE 95.
339. A new Markov chain analysis for a GA using symbolic representation. S.Chung and R.Perez, abstract for IEEE 95.
340. Genetic logic programming, T.R.Osborn, A.Charif, R.Lamas and E.Dubossarsky, abstract for IEEE 905.
341. An artificial life model for predicting the tertiary structure of unknown proteins that emulates the folding process. R. Calabretta, S. Nolfi and D Parisi.
342. The ant colony metaphor for searching continuous design spaces. G Bilchev and IC Parmee
343. Database support for computational chemistry: position paper prepared for TI Open OODB Workshop II, Judith B. Cushing, David M DeVaney, David Maier and David Feller.
344. Artificial neural networks recognise the magic numbers, submtd to Phys. Rev. Letters June 1991, H.Bohr, J.W.Clark and S.Gazula.
345. A brief comparison of some evolutionary optimization methods. Andy Keane.
346. Experiences with optimizers in structural design. A.J.Keane
347. Improved self-organizing maps with application to information retrieval. Michael Rozmus
348. Ant-Q: a reinforcement learning approach to the travelling salesman problem. Luca M. Gambardella and Marco Dorigo.
349. Integrating local search into Genetic Algorithms, Colin Reeves and Christian Holn, presented at the Applied Decision Technologies conference, Brunel University April 1995.
350. Distributing virtual worlds in a teleteaching environment. Klaus Rebensburg, Dirk Hetzer, Karl Jonas, Manfred Kaul and Josef Schafer.
351. SYNGEN Program for synthesis design: basic computing techniques; James Hendrickson and Glenn Toczko, J. Chem. Inf. Comput. Sci., 1989, 29, 137-145.
352. Automated pipe route generation using genetic algorithms, Dae Gyu Kim (M.Sc thesis, U. of Edinburgh, 1995).
353 Constructivism and science education: some epistemological problems Michael Matthews, J. Sci . Ed. and Technology, Vol 2 No 1 1993, 359.
354 ANT-Q A reinforcement learning approach to combinatorial optimization. Marco Dorigo and Luca Maria Gambardella, Technical report 95-01, IRIDIA.
355. Army ants: a collective intelligence. Nigel R. Franks American Scientist, March-April 1989, 139.
356. Artificial Intelligence Research Branch 1991 progress report and future plans. NASA, Moffett Field CA., April 1991
357. Competing concepts in CAOS, Alf Dengler et al Recl. Trav. Chim. Pays-Bas, 111 262-269 (1992)
358. Evolutionary divide and conquer, Luis F.G.Hernandez, MSc Thesis, Edinburgh University, 1995.
359. Rotating chemical reactions, Arthur T. Winfree (Scientific American?)
360. An efficient approach toward a flexible and general knowledge definition and program control language system for a synthesis-planning program. Wolf-Dietrich Ihlenfeldt, J. Chem. Inf. Comput. Sci. 1994, 34, 872-880
361. Navigating complex labyrinths: optimal paths from chemical waves. Oliver Steinbock, Agotha Toth and Kenneth Showalter, Science 267, 1995, p868.
362. Spiral waves in a model of the ferroin catalyzed Belousov-Zhabotinski reaction. A.B.Rovinsky, J. Phys. Chem. 90, 1986, p217.
363 Computer assisted organic synthesis (issue of Receuil des Travuax Chmiques des Pays-Bas, June 1992.)
364. Approaching the logic of synthesis design, James B. Hendrickson, Acc. Chem. Res. 1986, 19, 274-281.
365. Computer aids to synthesis planning. A. Peter Johnson, Chemistry in Britain, January 1985, p59.
366. A new treatment of chemical reactivity: development of EROS, an expert system for reaction prediction and synthesis design. Johann Gasteiger et al. Topics in current chemistry, vol 137, p21.
367. Organic synthesis in the age of computers, James B. Hendrickson, Angew. Chem. Intl. Ed. Engl. 29 (1990), 1286-1295.
368. The genetic algorithm in science. Hugh M. Cartwright, Pestic. Sci. 1995, 45, 171-178.
369. The discovery of synthetic routes by computer. Herbert Gelernter et al,
370. MAPOS: a computer program for organic synthesis design based on synthon model of organic chemistry. Ludek Matyska and Jaroslav Koca, J. Chem. Inf. Comput. Sci. 1991, 31, 380-386.
371. Cationic polyene cyclizations. A computer assisted synthesis approach. Jan-Willem Boiten and Jan H. Noordik, J. Chem. Inf. Comput. Sci., 1993, 33, 727-735.
372 Computer-assisted analysis and perception of stereochemical features in organic molecules using the CHIRON program. Stephen Hanessian et al, J. Chem. Inf. Comput. Sci., 1990, 30, 413-425
373. The generation of reaction networks with RAIN. 1 The reaction generator. Eric Fontain and Klaus Reitsam, J. Chem. Inf. And Comput. Sci., 1991, 31, p96.
374. The problem of atom-to-atom mapping. An application of genetic algorithms. E.Fontain, Analytica Chimica Acta 265 (1992) 227-232.
375. The generation of reaction networks with RAIN 2. Resonance structures and tautomerism. Eric Fontain, Tetrahedron Computer Methodology, 3, 469-477 1990.
376. The B6H14 problem: generation of a catalogue of conceivable isomers. Eric Fontain, Heteroatom chemistry 5, 1994, p61.
377 The LILITH approach to organic synthesis planning. Luca Baumer, Giordano Sala and Guido Sello, Analytica Chimica Acta 235 (1990) 209-214.
378. Self-adapting computer program system for designing organic syntheses. Z. Hippe, Analytica Chimica Acta, 133 (1981) 677-683.
379. Free energy calculations involving NH4+ in water. Stephane Boudon and Georges Wipff, J. Comput. Chem., 12, 42-51 (1991)
380. Generating neural networks with genetic algorithms using a marker based coding. Tom Walker, MSc thesis, University of Edinburgh, 1995.
381. Adapting operator probabilities in genetic algorithms. Andrew Tuson, MSc thesis, University of Edinburgh, 1995.
382. Investigation of a multiple chromosome evolutionary algorithm for bus driver scheduling and other problems. Emma Collingwood. MSc thesis, University of Edinburgh, 1995.
383. Using Java to animate the vibrations of molecules: Calculation and visualization of molecular vibrations in (NSF)3 J.L.Yarger, A.Chizmeshya and B.K.Yarger. Subt to Chemical Educator, August 1996.
384. Physical chemist5ry on-line: A small scale intercollegiate interactiuve learning experiencve. G.Long, R.Howald, C.A.Miderski and T.J.Zielinski, Submt to Chemical Educator, summer 1996.
385. Development and delivery of chemical education hypermedia using the world-wide web. Brian Tissue, Paper 4, ChemConf 1996.
386. The role of molecular structure and modelling in general chemistry. L.L.Jones , paper 3, ChemConf 1996.
387. WIZARD: AI in conformational analysis. D.P.Dolata, A.R.Leach and K.Prout, J. Comp.-aided Molecular Design, 1 (1987) 73-85
388. Computer-assisted knowledge acquisition system for synthesis planning. T.Nakayama, J. Chem. Inf. Comp. Sci. 1991, 31, 495-503.
389. The royal road functions: description, intent and experimentation. R.J.Quick, V.YJ.Rayward-Smith and G.D.Smith.
390. Stepping stones and hidden haystacks: When a genetic algorithm defeats a hill-climber. D.Corne and P.Ross.
391. Computer-assisted solution of chemical problems - The historical perspective and the present state of the art of anew discipline of chemistry. I.Ugi et al. Angew. Chem. Int. Ed. Engl. 1993, 32 201-227.
392. Artificial intelligence in chemistry. N.A.B.Gray, Analytica Chimica Acta 210 (1988) 9-32.
393. Artificial intelligence in organic synthesis. SST Starting material selection strategies. An application of superstructure search. W.Todd Wipke and D.Rogers, J. Chem. Inf. Comput. Sci. 1984 24, 71-81.
394. Learning the "Next" dimension. G.Bilchev and I.C.Parmee.
395. An analysis of genetic programming. Una-May O'Reilly, PhD thesis, Carleton University, September 1995
396. SOM_PAK The self-ordering map program package, version 1.2 SOM programming team, Helsinki University of Technology, 1992.
397. A PERL script to generate HTML pages containing multiple-choice questions. Ronald Earp and Brian Tissue. Subtd. to Chemical Educator, August 1996.
398. Finding interesting rules from large sets of discovered association rules. Mila Klemettinen et al. 3rd Int. Conf. on Information and Knowledge Management, pp 401-407, 1994.
399. Biologically inspired computational ecologies: a case study. P.Devine and R.C.Paton. (Subt to AISB 97).
400. MSc thesis from Edinburgh on SOMs -author unknown.
401. Spatial reasoning with genetic algorithms. An application in planning of safe liquid petroleum gas sites. Ken Lunn and Caroline Johnson, subt to AISB 96
402. Characterizing signal behaviour using genetic programming Per Jonsson and Jonas Barklund, subt. to AISB 96.
403. Generation of structured process models using genetic programming. H.Pohlheim and P.Marenbach. subt to AISB 96
404 Evolving software test data - GA's learn self expression. Jim Smith and T.C.Fogarty. subt to AISB 96.
405. Machine learning: a mathematical framework for neural network, symbolic and genetics-based learning. G. Deon Oosthuizen. Origin unknown.
406. A comparative evaluation of search methods applied to parametric design of aircraft. Mark Bramlette and Rod Cuisic, ICGA 89
407. A comparative evaluation of search methods applied to parametric design of aircraft. Mark Bramlette and Rud Cusic (similar to paper 406).
408. Artificial neural networks that learn many-body physics. John Clark and Srinivas Gazula. Condensed matter theories Vol 6, Plenum NY, 1991, pp 1-24..
409. A genetic algorithm for job-shop problems with various schedule quality criteria. Hsiao-Lan Fang, David Corne and Peter Ross. subt to AISB 96
410. CFS-C/FSW1 An implementation of the CFS-C Classifier system in a task-domain that involves learning to traverse a finite state world. Rick Riolo.
411. LETSEQ an implementation of the CFS-C classifier system in a task domain that involves learning to predict letter sequences. Rick Riolo.
412 Genetic algorithms and flowshop scheduling: towards the development of a real-time process control system. Hugh Cartwright and Andrew Tuson, post workshop proceedings of AISB workshop, Leeds 1994.
413 Using Netscape as a presentation manager. Scott Van Bramer. ChemConf 97.
414 What every chemist should know about computers II. Mary Swift and Theresa Julia Zielinski. ChemConf 97
415 The costs of incorporating information technology in education. Brian Tissue, ChemConf 97
416 Bacterial transformation of polychlorinated biphenyls Donna Bedard, Biotechnology and biodegradation.
417 extensive degradation of aroclors and environmentally transformed polychlorinated biphenyls by Alcaligenes eutrophus H850. Donna Bedard et al, Applied and Environmental microbiology, May 1987,m pp1094-1102
418 Rapid assay for screening and characterising micro-organisms for the ability to degrade polychlorinated biphenyls. Donna Bedard et al. Applied and Environmental microbiology, 1986, pp-761-768419 A graphical presentation of the composition and properties of chlorinated dibenzodioxins and dibenzofurans, V.Zikto, The Science of the total environment, 93 (1989) 191-194.
420 Display of the composition of polychlorinated biphenyls. Vladimir Zitko, Anal. Chem. Vol 60, No18 1998-2000421 Influence of chlorine substitution pattern on the degradation of polychlorinated biphenyls by eight bacterial strains. Donna Bedard and Marie Haberl. Microb. Ecol. (1990) 20: 87-102
422 Evidence for novel mechanisms of polychlorinated biphenyl metabolism in Alcaligenes eutrophus H850, Donna Bedard et al. Appl. and Env. Microbioplogy, May 1987, pp 1103-1112
423 Prediction of biodegradability of organic chemical by an artificial neural network. V.Zitko, Chemosphere Vol 23, No 3, pp305-312.
424 Use of gas chromatograms of the essential leaf oils of the Genus Eucalyptus for taxonomic purposes Peter Dunlop, Caroline Bignell and Brynn Hibbert, Aust. J. Bot 1997 45, 1-13.
425 The computer as a materials science benchamark. Dean Campbell et al. J. Chem. Educ. Vol 75 No 3, March 1988.
426 Development of a model for the classification of toxin-induced lesions using proton NMR spectroscopy of urine combined with pattern-recognition. E.Holmes et al.
427 Analysis of hidden units in a layered network trained to classify sonar targets. R.Paul Gorman and T.J.Sejnowski
428 Constraint database: promising technology or just intellectual exercise? Constraints - an International Journal, 2, 35-44 (1997) A.Brodsky
429. Constraint programming for combinatorial search problems. Constraints, an International Journal, 2, 99-1091 (1997) P Van Hentenryck
430 Constraint-based design of embedded intelligent systems. Constraints, 2, 83-86 (1997), A.K.Mackworth.
431 Collective patterns and decision-making Ethology, Ecology and Evolution 1: 295-311 (1989) J.L.Deneubourg and S.Goss.
432 Probabilistic behaviour in ants: A strategy of errors? J. Theor. Biol. (1983) 105, 259-271. J.L. Deneunbourg et al.
433 Introduction to genetics-based machine learning. Chapter from Machine Learning, Search and Optimization by David Goldberg.
434 Learning classification rules from an ion chromatography database using a genetic based classifier system. van Kampen et al, Analytica Chimica Acta 344 (1997) 1-15.
435 Fuzzy control of pH using Genetic Algorithms. Charles Karr and Edward Gentry. IEEE Trans on Fuzzy Systems Vol 1 No 1 (1993) pp 46-53
436 Selecting fuzzy if-then rules for classification problems using genetic algorithms. Hisao Ishibuchi et al. IEEE Trans on fuzzy systems Vol 3 (3) (1995) pp 260-270.
437 Generating fuzzy rules for target tracking using a steady state genetic algorithm. Keith Chan, Vika Lee and Henry Leung. IEEE Trans on evolutionary computation, Vol 1(3) pp 189-199.
438 FuGeNeSys a fuzzy genetic neural system for fuzzy modeling. Msarco Russo. IEEE Trans on fuzzy systems, Vol 6(3) (1998) pp 373-387.
439 Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. A Homaifar and E McCormick. IEEE Trans. on fuzzy systems. Vol 3(2) (1995) pp 129-138.
440 New design and stability analysis of fuzzy proportional-derivative control systems. Heidar Malki et al. IEEE Trans on fuzzy systems. Vol 2(4) (1994) pp 245-254.
441 Neuro-fuzzy hybrid control system of tank level in petroleum plant. Tani et al IEEE Trans on fuzzy systems. Vol 4(3) (1996) pp 360-368.
442 Physical Chemistry On-line: Maximizing your potential. Deborah Sauder and Marcy Towns, CONFCHEM Summer 99 conference.
443. Application of genetic algorithms for the design of ozone control strategies. D.H.Loughlin et al (North Carolina State University - submitted to J. Air & Waste Manag. Assn.)
444. Multiple points of view of distance education in chemistry: chemist, practitioner, cursed administrator. Joseph S. Merola, CONFCHEM 1999.