Jonathan L. Shapiro

Jonathan Shapiro

2007
35	EE	Jürgen Branke, Clemens Lode, Jonathan L. Shapiro: Addressing sampling errors and diversity loss in UMDA. GECCO 2007: 508-515
34	EE	Hao Wu, Jonathan L. Shapiro: Parameter cross-validation and early-stopping in univariate marginal distribution algorithm. GECCO 2007: 632-633
33	EE	Chong Liu, Jonathan Shapiro: Implementing Classical Conditioning with Spiking Neurons. ICANN (1) 2007: 400-410
32	EE	Stephen B. Furber, G. Brown, Joy Bose, J. Mike Cumpstey, P. Marshall, Jonathan L. Shapiro: Sparse Distributed Memory Using Rank-Order Neural Codes. IEEE Transactions on Neural Networks 18(3): 648-659 (2007)
2006
31	EE	Hao Wu, Jonathan L. Shapiro: Does overfitting affect performance in estimation of distribution algorithms. GECCO 2006: 433-434
30	EE	Jonathan Shapiro: Programming language challenges in systems codes: why systems programmers still use C, and what to do about it. PLOS 2006: 9
29	EE	Jonathan L. Shapiro: Diversity Loss in General Estimation of Distribution Algorithms. PPSN 2006: 92-101
28	EE	Elon S. Correa, Jonathan L. Shapiro: Model Complexity vs. Performance in the Bayesian Optimization Algorithm. PPSN 2006: 998-1007
27	EE	Joy Bose, Stephen B. Furber, Jonathan L. Shapiro: An associative memory for the on-line recognition and prediction of temporal sequences CoRR abs/cs/0611020: (2006)
2005
26	EE	Joy Bose, Stephen B. Furber, Jonathan L. Shapiro: A Spiking Neural Sparse Distributed Memory Implementation for Learning and Predicting Temporal Sequences. ICANN (1) 2005: 115-120
25		Hao Wu, Jonathan L. Shapiro: Choosing Search Algorithms in Bayesian Optimization Algorithm. IEC (Prague) 2005: 51-55
24	EE	Joy Bose, Stephen B. Furber, Jonathan L. Shapiro: A System for Transmitting a Coherent Burst of Activity Through a Network of Spiking Neurons. WIRN/NAIS 2005: 44-48
23	EE	Jonathan L. Shapiro: Drift and Scaling in Estimation of Distribution Algorithms. Evolutionary Computation 13(1): 99-123 (2005)
22	EE	Stephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: On-line novelty detection for autonomous mobile robots. Robotics and Autonomous Systems 51(2-3): 191-206 (2005)
2003
21	EE	Jason Fleischer, Stephen Marsland, Jonathan Shapiro: Sensory Anticipation for Autonomous Selection of Robot Landmarks. Anticipatory Behavior in Adaptive Learning Systems 2003: 201-221
2002
20	EE	Jonathan L. Shapiro: Scaling of Probability-Based Optimization Algorithms. NIPS 2002: 383-390
19		Tom Duckett, Stephen Marsland, Jonathan Shapiro: Fast, On-Line Learning of Globally Consistent Maps. Auton. Robots 12(3): 287-300 (2002)
18	EE	Stephen Marsland, Jonathan Shapiro, Ulrich Nehmzow: A self-organising network that grows when required. Neural Networks 15(8-9): 1041-1058 (2002)
2001
17	EE	Andrew Johnson, Jonathan L. Shapiro: The Importance of Selection Mechanisms in Distribution Estimation Algorithms. Artificial Evolution 2001: 91-103
16	EE	Jonathan L. Shapiro: Genetic Algorithms in Machine Learning. Machine Learning and Its Applications 2001: 146-168
15	EE	Jonathan L. Shapiro, J. Wearden: Reinforcement Learning and Time Perception -- a Model of Animal Experiments. NIPS 2001: 115-122
2000
14		Tom Duckett, Stephen Marsland, Jonathan Shapiro: Learning Globally Consistent Maps by Relaxation. ICRA 2000: 3841-3846
13	EE	Stephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: Novelty Detection for Robot Neotaxis CoRR cs.RO/0006005: (2000)
12	EE	Stephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: A Real-Time Novelty Detector for a Mobile Robot CoRR cs.RO/0006006: (2000)
11	EE	Stephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: Novelty Detection on a Mobile Robot Using Habituation CoRR cs.RO/0006007: (2000)
1998
10	EE	Jonathan L. Shapiro: Does Data-Model Co-evolution Improve Generalization Performance of Evolving Learners? PPSN 1998: 540-549
1997
9		David Corne, Jonathan L. Shapiro: Evolutionary Computing, AISB International Workshop, Manchester, UK, April 7-8, 1997, Selected Papers Springer 1997
8		Sybil Hirsch, Jonathan L. Shapiro, Peter I. Frank: Use of an Artificial Neural Network in Estimating Prevalence and Assessing Underdiagnisis of Asthma. Neural Computing and Applications 5(2): 124-128 (1997)
1996
7		Jonathan Shapiro, Adam Prügel-Bennett: Genetic Algorithm Dynamics in a Two-well Potential. FOGA 1996: 101-116
6		Magnus Rattray, Jonathan Shapiro: Noisy Fitness Evaluation in Genetic Algorithms and the Dynamics of Learning. FOGA 1996: 117-139
1995
5		Jonathan L. Shapiro, Adam Prügel-Bennett: Maximum Entropy Analysis of Genetic Algorithm Operators. Evolutionary Computing, AISB Workshop 1995: 14-24
1994
4		Jonathan Shapiro, Adam Prügel-Bennett, Magnus Rattray: A Statistical Mechanical Formulation of the Dynamics of Genetic Algorithms. Evolutionary Computing, AISB Workshop 1994: 17-27
1993
3	EE	Jonathan L. Shapiro, Adam Prügel-Bennett: Non-Linear Statistical Analysis and Self-Organizing Hebbian Networks. NIPS 1993: 407-414
1990
2	EE	Jonathan Shapiro, Peter Mowforth: Data Fusion in 3D Through Surface Tracking. IEA/AIE (Vol. 1) 1990: 163-168
1	EE	Jonathan Shapiro, Jin Zhengping: An Interactive Colour Line Recognition System for Seismic Section Digitisation. MVA 1990: 223-226

Coauthor Index

1	Joy Bose	[24] [26] [27] [32]
2	Jürgen Branke	[35]
3	G. Brown	[32]
4	David W. Corne (David Corne)	[9]
5	Elon S. Correa	[28]
6	J. Mike Cumpstey	[32]
7	Tom Duckett	[14] [19]
8	Jason Fleischer	[21]
9	Peter I. Frank	[8]
10	Stephen B. Furber (Steve Furber)	[24] [26] [27] [32]
11	Sybil Hirsch	[8]
12	Andrew Johnson	[17]
13	Chong Liu	[33]
14	Clemens Lode	[35]
15	P. Marshall	[32]
16	Stephen Marsland	[11] [12] [13] [14] [18] [19] [21] [22]
17	Peter Mowforth	[2]
18	Ulrich Nehmzow	[11] [12] [13] [18] [22]
19	Adam Prügel-Bennett	[3] [4] [5] [7]
20	Magnus Rattray	[4] [6]
21	J. Wearden	[15]
22	Hao Wu	[25] [31] [34]
23	Jin Zhengping	[1]

Colors in the list of coauthors