I am very pleased to have been appointed to be the new Editor of the AIEDAM journal: only the third in its 15 years of existence. My involvement with AIEDAM started early in the life of the journal, due to my professional interactions with Professor Clive Dym, its first Editor. Clive put the journal on a very sound footing, and set the high standards that the journal continues to uphold. That it has maintained such a high reputation in the fields that it covers is due to Professor William Birmingham, the second Editor. My goal is to continue with the policies developed by these two prior Editors, while striving to raise the journal's standards, reputation, and visibility even further.

This paper investigates the use of typological knowledge in the visual modality through a computer framework that combines multidisciplinary technologies from computer science, that is, artificial intelligence, software engineering, database system, and programming language, to help provide solutions and services to building designers. The solving of design problems frequently involves visual thinking, which has to do with the intensive use of visual knowledge like pictures, images, and other types of visual displays. The recognized power of typological knowledge in design problem solving is applied to support the exploration of a diversity of possible design solutions represented in a pictorial mode. The innovative use of computer science technologies enables a smooth link of visual typological knowledge with the design goals. Within the framework, a core technology was designed to respond to a designer's specific needs through dynamic user viewpoint generation, so that design solutions are associated with relevant (retrieved) visual typologies from the knowledge base. This has been achieved in a two-way process, in which the designer establishes an interactive dialogue with an experimental computerized framework.

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

This paper proposes a new logical framework for vehicle route-sequence planning of passenger travel requests. Each request is a fetch-and-send service task associated with two request-locations, namely, a source and a destination. The proposed framework is developed using propositional linear time temporal logic of Manna and Pnueli. The novelty lies in the use of the formal language for both the specification and theorem-proving analysis of precedence constraints among the location visits that are inherent in route sequences. In the framework, legal route sequences—each of which visits every request location once and only once in the precedence order of fetch-and-send associated with every such request—is formalized and justified, forming a basis upon which the link between a basic precedence constraint and the corresponding canonical forbidden-state formula is formally established. Over a given base route plan, a simple procedure to generate a feasible subplan based on a specification of the forbidden-state canonical form is also given. An example demonstrates how temporal logic analysis and the proposed procedure can be applied to select a final (feasible) subplan based on additional precedence constraints.

In this paper, we propose a population-based evolutionary multiobjective optimization approach to design combinational circuits. Our results indicate that the proposed approach can significantly reduce the computational effort required by a genetic algorithm (GA) to design circuits at a gate level while generating equivalent or even better solutions (i.e., circuits with a lower number of gates) than a human designer or even other GAs. Several examples taken from the literature are used to evaluate the performance of the proposed approach.