s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

We present an orthogonal graph drawing algorithm that uses a sketchy drawing of the graph as input. While the algorithm produces an orthogonal drawing with few bends in the Kandinsky model it also preserves the general appearance of the sketch. Potential applications for this kind of drawing algorithm include the generation of schematic maps from geographic networks and interactive orthogonal graph drawing.

Card-handling using hypertext editor can be apowerful methodology for generation of ideas or understanding of complex problems. To support such activity, recognizing implicit structure in the arrangement of cards would be useful. But, because the structures to be recognized areby nature ambiguous and highly dependent on user-specific perception, it is difficult for conventional rule-based spatial parsing algorithm to achieve this task. We propose techniques for building spatial parser suitable for finding such ambiguous structures based on the mechanics of human perception. Moreover, our parser is adaptively customized to reflect aparticular user's preferences through an interactive suggestion process, supported by application of a genetic algorithm.

This paper and the accompanying demo describe a strategy and a software architecture for integrating several declarative approaches. This architecture allows for the interactive specification of local criteria for each vertex and edge. The Gold

Constraints have been playing an important role in the user interface field since its infancy. A prime use of constraints in this field is to automatically maintain geometric layouts of graphical objects. To facilitate the construction of constraint-based user interface applications, researchers have proposed various constraint satisfaction methods and constraint solvers. Most previous research has focused on either local propagation or linear constraints, excluding more general nonlinear ones. However, nonlinear geometric constraints are practically useful to various user interfaces, e.g., drawing editors and information visualization systems. In this paper, we propose a novel constraint solver called Chorus, which realizes various powerful nonlinear geometric constraints such as Euclidean geometric, non-overlapping, and graph layout constraints. A key feature of Chorus is its module mechanism that allows users to define new kinds of geometric constraints. Also, Chorus supports "soft" constraints with hierarchical strengths or preferences (i.e., constraint hierarchies). We describe its framework, algorithm, implementation, and experimental results. KEYWORDS: geometric constraints, soft constraints, constraint solvers, module mechanisms, graph layouts

The paper presents self-organizing graphs, a novel approach to graph layout based on a competitive learning algorithm. This method is an extension of selforganization strategies known from unsupervised neural networks, namely from Kohonen's self-organizing map. Its main advantage is that it is very flexibly adaptable to arbitrary types of visualization spaces, for it is explicitly parameterized by a metric model of the layout space. Yet the method consumes comparatively little computational resources and does not need any heavy-duty preprocessing. Unlike with other stochastic layout algorithms, not even the costly repeated evaluation of an objective function is required. To our knowledge this is the first connectionist approach to graph layout. The paper presents applications to 2D-layout as well as to 3D-layout and to layout in arbitrary metric spaces, such as networks on spherical surfaces. 1 Introduction Automatic layout techniques are a crucial component for any application which...

We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated annealing and genetic algorithms, automatic layout systems can find a good layout using an evaluation function which can calculate how good a given layout is. However, the evaluation function is usually not known beforehand, and it might vary from user to user. In our system, users show the system several pairs of good and bad layout examples, and the system infers the evaluation function from the examples using genetic programming technique. After the evaluation function evolves to reflect the preferences of the user, it is used as a general evaluation function for laying out graphs. The same technique can be used for a wide range of adaptive user interface systems.

Graph Drawing addresses the problem of finding a layout of a graph that satisfies a given aesthetic objective. This problem is very important for programs that visualize diagrams such as those used in Software Engineering methodologies, Data Mining functional dependencies, Semantic and Bayesian Networks, etc. This paper is concerned with developing a general approach for Graph Drawing based on Evolutionary Algorithms. We use (1+1)-Evolution Strategies which is justified by previous work. Experimental results on 146 graphs demonstrate the algorithms capacity for solving the most important problem in the graph drawing field: edge-crossings minimization. In addition, a preliminary study has shown how to acquire and satisfy subjective user criteria. The algorithm has shown to be fast enough what allows its use in real-world programs that visualize diagrams.

For applications which generate diagrammatic representations automatic layout techniques are a crucial component. Since graph-like network diagrams are among the most commonly used and most important types of diagrammatic displays, layout techniques for graphs have been extensively studied. However, a problem with current graph layout methods which are capable of producing satisfactory results for a wide range of graphs is that they often put an extremely high demand on computational resources. This paper introduces a new layout method that consumes only little computational resources and does not need any heavyduty preprocessing. Unlike other declarative layout algorithms not even the costly repeated evaluation of an objective function is required. The method presented is based on a competitive learning algorithm which is an extension of self-organization strategies known from unsupervised neural networks. 1 Introduction For applications which generate diagrammatic representations a...

: A general overview of the problem of automatically generating aesthetically pleasing drawings of graphs is presented. Requirements particular to diagrams in Rational Rose is discussed. The Sugiyama layout algorithm and the Spring Embedder algorithm together with a number of proposed modifications and improvements to these algorithms are discussed. Examples of drawings generated by these algorithms are presented. Automatic layout of diagrams through the use of genetic algorithms is also discussed. Supervisor: Gunnar Blomberg, Rational Software Scandinavia AB Examiner: Mats Nordstrm, Computing Science Dept., Uppsala University Passed: Contents 1 Introduction 1 1.1 Definition of Terms 2 1.2 Background 3 1.3 Problem Description 4 1.4 General Difficulties 6 2 Different Approaches 7 2.1 Algorithmic Approach 7 2.2 Declarative Approach 8 3 Examples of Algorithmic Approaches 9 3.1 Sugiyama Algorithm 9 3.1.1 Phase 1 Revisited -- Preprocessing 10 3.1.2 Phase 2 Revisited -- Edge Crossing Mini...