This paper gives a brief introduction into multiple objective programming support. We will overview basic concepts, formulations, and principles of solving multiple objective programming problems. To solve those problems requires the intervention of a decisionmaker. That’s why behavioral assumptions play an important role in multiple objective programming. Which assumptions are made affects whi...

John Searle has argued that functions owe their existence to the value that we put into life and survival. In this paper, I will provide a critique of Searle’s argument concerning the ontology of functions. I rely on a standard analysis of functional predicates as relating not only a biological entity (e.g., the heart), an activity that constitutes the function of this entity (e.g., pumping blo...

Let (E,A) be a set system consisting of a finite collectionA of subsets of a ground set E, and suppose that we have a function φ which maps A into some set S . Now removing a subset K from E gives a restriction A(K̄) to those sets of A disjoint from K, and we have a corresponding restriction φ|A(K̄) of our function φ. If the removal of K does not affect the image set of φ, that is Im(φ|A(X̄)) = Im...

Many neural networks can be derived as optimization dynamics for suitable objective functions. We show that such networks can be designed by repeated transformations of one objective into another with the same xpoints. We exhibit a collection of algebraic transformations which reduce network cost and increase the set of objective functions that are neurally implementable. The transformations in...

Objectives . A frequently used method for obtaining Pareto-optimal solutions is to minimize a selected quality index under restrictions of the other indices, whose values are thus preset. For scalar objective function, global minimum sought that contains restricted indices as penalty terms. However, landscape such function has steep-ascent areas, which significantly complicate search minimum. T...

Fuzzy clustering comprises a family of prototype-based clustering methods that can be formulated as the problem of minimizing an objective function. These methods can be seen as “fuzzifications” of, for example, the classical c-means algorithm, which strives to minimize the sum of the (squared) distances between the data points and the cluster centers to which they are assigned. However, it is ...

We propose and evaluate a class of objective funct ions tha t rank hypotheses for feature labels. Our approach takes into account the representation cost and qual i ty of the shapes themselves, and balances the geometric requirements against the photometr ic evidence Th is balance is essential for any system using underconstrained or generic feature models. We introduce examples of specific mod...

The effectiveness of traditional CAD optimization algorithms is proportional to the accuracy of the targeted objective functions. However, behavioral synthesis tools are not used in isolation; they form a strongly connected design flow where each tool optimizes its own objective function without considering the consequences on the optimization goals of the subsequently applied tools. Therefore,...