Gabriella Kókai

Using Hardware-Based Particle Swarm Method for Dynamic Optimization of Adaptive Array Antennas

The following article describes and discusses the suitability of the particle swarm optimization (PSO) for the employment with blind adaptation of the directional characteristic of array antennas. By means of extensive simulations it was confirmed that the suggested PSO is able to follow dynamic changes in the environment. Based on these results a concept is discussed for a high-parallel optimizing procedure as distributed logic in application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). Thus an online procedure is available for time-critical applications of the adaptive beam forming

Modelling Blood Vessels of the Eye with Parametric L-Systems Using Evolutionary Algorithms

In this paper the GREDEA system is presented. The main idea behind it is that with the help of evolutionary algorithms a grammatical description of the blood circulation of the human retina can be inferred. The system uses parametric Lindenmayer systems as description language. It can be applied on patients with diabetes who need to be monitored over long periods.

GIDTS: a graphical programming environment for Prolog

This paper puts forward the Graphical Interactive Diagnosing, Testing and Slicing System (GIDTS) which is a graphical programming environment for PROLOG programs. The IDTSpart of the system integrates Shapiros Interactive Diagnosis Algorithm with the Category Partition Testing Method (CPM) and a slicing technique performing the algorithmic debugging and functional testing of PROLOG programs. The integration of IDTS with a graphical user interface (GUI) supports the whole functionality of IDTS and provides a user-friendly environment giving the user more information on the state of the debugging process. GIDTS extends IDTS to a complete programming environment. It allows one to handle the debugging of complex programs using the extended syntax and semantics of PROLOG in a very flexible way. A static code diagnosis has also been implemented. In addition GIDTS supports debugging-directed editing of the source program, and a quick source code navigation via any of the tools (for example: the debugger, the static call graph and the information retriever). All these features are supported by the graphical user interface.

Self-adaptive ant colony optimisation applied to function allocation in vehicle networks

Modern vehicles possess an increasing number of softwareand hardware components that are integrated in electroniccontrol units (ECUs). Finding an optimal allocation forall components is a multi-objective optimisation problem,since every valid allocation can be rated according to multipleobjectives like costs, busload, weight, etc. Additionally,several constraints mainly regarding the availability of resourceshave to be considered. This paper introduces a newvariant of the well-known ant colony optimisation, whichhas been applied to the real-world problem described above.Since it concerns a multi-objective optimisation problem,multiple ant colonies are employed. In the course of thiswork, pheromone updating strategies specialised on constrainthandling are developed. To reduce the effort neededto adapt the algorithm to the optimisation problem by tuningstrategic parameters, self-adaptive mechanisms are establishedfor most of them. Besides the reduction of theeffort, this step also improves the algorithms convergencebehaviour.

Evolving swarms that build 3D structures

The complex interactions of natural swarms, for example formed by some social insects, are difficult to comprehend. Considering tasks such as nest-building, the necessary underlying communication presumably happens indirectly by changing and reacting on the environment. This paper presents an overall approach to interactively evolve rule-based swarms that create three-dimensional structures in continuous space. The approach comprises the design of the swarm agent, details about the breeding process and first results. A swarm is determined by a set of flocking parameters and a set of instructional rules that allow the agents to change their local structural environment. The center or focus of the swarms endeavour may be shifted either on a swarm agent or on a fixed point in space. The alteration of the supplied 3D structure during the course of evolution enables an external supervisor to interactively guide the development of a swarm.

Evolving artificial trees described by parametric L-systems

Describes a solution of the inverse problem for parametric Lindenmayer systems (L-systems) with genetic algorithms. The inverse problem studied is that of evolving Lindenmayer grammars to describe plants. A genetic algorithm is used to evolve the rewriting rules and the set of system parameters. In this paper, we present on extension of the solution of G. Ochoa (1998) and K.J. Mock (1998), by which genetic algorithms are applied to simulate the evolution of artificial plant morphologies. Three main improvements are described: (1) more populations are processed in parallel; (2) an adaptation scheme for the application probability of genetic operators is applied; (3) the turtle graphics system is modified so that 3D structures such as trees can be evolved and drawn.

GeLog - A System Combining Genetic Algorithm with Inductive Logic Programming

We have developed a genetic logic programming system (GeLog) which implements a combination of two different approaches for automatic programming: inductive logic programming and genetic algorithm. The paper presents the system and discusses its performance on a benchmark problem.

Genetic Algorithms to Improve Mask and Illumination Geometries in Lithographic Imaging Systems

This paper proposes the use of a genetic algorithm to optimize mask and illumination geometries in optical projection lithography. A fitness function is introduced that evaluates the imaging quality of arbitrary line patterns in a specified focus range. As a second criterion the manufacturability and inspectability of the mask are taken into account. With this approach optimum imaging conditions can be identified without any additional a-priori knowledge of the lithographic process. Several examples demonstrate the successful application and further potentials of the proposed concept.

Will Darwin's law help us to improve our resist models?

Calibration of resist model parameters becomes more and more important in lithography simulation. The general goal of such a calibration procedure is to find parameters and model options which minimize the difference between experimentally measured and simulated data. In this paper a multidimensional downhill simplex method and a genetic algorithm are introduced. We investigate the performance of different modeling options such as the diffusivity of the photogenerated acid and of the quencher base, and different development models. Furthermore, new objective functions are proposed and evaluated: The overlap of process windows between simulated and experimental data and the comparison of linearity curves. The calibration procedures are performed for a 248nm and for a 193nm chemically amplified resist, respectively.

Grammatical Retina Description with Enhanced Methods

In this paper the enhanced version of the GREDEA system is presented. The main idea behind the system is that with the help of evolutionary algorithms a grammatical description of the blood circulation of the human retina can be inferred. The system uses parametric Lindenmayer systems as description language. It can be applied on patients with diabetes who need to be monitored over long periods of time. Since the first version some improvements were made, e.g. new fitness function and new genetic operators. In this paper these changes are described.