Lawrence Davis · VLDB 2026 Research

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 8 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 4 papers		Kernel, tree and ensemble methods · 33% Optimization for machine learning · 32% Deep learning architectures and training · 19%
Theoretical computer science 1 paper		Mathematical optimization · 100%
Software engineering, system software, and programming languages 1 paper		Programming languages and type systems · 77% Operating systems · 23%
Human-computer interaction and pervasive computing 1 paper		User interface design and tools · 100%

Topics — the 9 heaviest of 11, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Machine learning › Kernel, tree and ensemble methods classifier systems	2	1988	Mapping Classifier Systems Into Neural Networks · NIPS 1988 Classifier Systems with Hamming Weights · ML 1988
Machine learning › Deep learning architectures and training › feedforward neural network feedforward neural network training	1	1989	Training Feedforward Neural Networks Using Genetic Algorithms · IJCAI 1989
Mathematical optimization evolutionary computation	1	1989	Training Feedforward Neural Networks Using Genetic Algorithms · IJCAI 1989
Mathematical optimization › evolutionary computation genetic algorithm	1	1989	Training Feedforward Neural Networks Using Genetic Algorithms · IJCAI 1989
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge-based systems rule-based systems	1	1988	Mapping Classifier Systems Into Neural Networks · NIPS 1988
Machine learning › Optimization for machine learning adaptive algorithm	1	1985	Applying Adaptive Algorithms to Epistatic Domains · IJCAI 1985
Machine learning › Optimization for machine learning evolutionary computation	1	1985	Applying Adaptive Algorithms to Epistatic Domains · IJCAI 1985
Machine learning › Optimization for machine learning › evolutionary computation genetic algorithms	1	1985	Applying Adaptive Algorithms to Epistatic Domains · IJCAI 1985
Programming languages and type systems language design	1	1985	Software innovations for the texas instruments explorer computer · Proc. IEEE 1985

Methods — techniques the papers use, named apart from their topics

genetic algorithm · 0.0menu-based natural language interface · 0.0neural network mapping · 0.0hamming weights · 0.0epistatic domains · 0.0adaptive algorithm · 0.0

Year	Publication	Venue	Position
2002	A Modified Classifier System Compaction Algorithm Chunsheng Fu, Lawrence Davis	GECCO	2
2002	Genetic Algorithms And Fine-grained Topologies For Optimization Lawrence Davis, Chunsheng Fu	GECCO	2
1992	Guess-and-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems Yuping Qiu, Louis Anthony Cox Jr., Lawrence Davis	UAI	3
1991	A Hybrid Genetic Algorithm for Classification James D. Kelly Jr., Lawrence Davis	IJCAI	2
1989	Training Feedforward Neural Networks Using Genetic Algorithms David J. Montana, Lawrence Davis	IJCAI	2
1988	Classifier Systems with Hamming Weights Lawrence Davis, David K. Young	ML	1
1988	Mapping Classifier Systems Into Neural Networks Lawrence Davis	NIPS	1
1985	Applying Adaptive Algorithms to Epistatic Domains Lawrence Davis	IJCAI	1
1985	Software innovations for the texas instruments explorer computer abstract Explorer is a LISP Machine from Texas Instruments. Its software starts with a vast body of software developed at MIT. Texas Instruments has stabilized that software and added more packages and utilities. This paper describes certain innovations in Texas Instrument's software for the Explorer. TI's software innovations were in two areas: increasing the learnability of this sophisticated system and adding software components as an aid to prototypers and software developers. Learning aid innovations are embodied in Suggestions Menus and the Universal Command Loop. Software component innovations are embodied in the Universal Command Loop, the Graphics Window System and Graphics Editor, the Relational Table Manager, and the Menu-Based Natural Language Interface. The innovations represented are of two kinds: innovations in concept(Suggestions Menus, Universal Command Loop, and Menu-Based Natural Language Interface) and innovations in design (Graphics Window System, Relational Table Manager). Harry R. Tennant, Roger R. Bate, Stephen M. Corey, Lawrence Davis, Paul Kline, Lamott G. Oren, M. Rajinikanth, Richard M. Saenz, Daniel Stenger, Craig W. Thompson	Proc. IEEE	4