International Journal on Artificial Intelligence Tools

Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. The problem-solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function. Here, we examine the role that different forms of knowledge can play in the self-adaptation process within cultural systems. In particular, we compare various approaches that use normative and situational knowledge in different ways to guide the function optimization process.

The results in this study demonstrate that Cultural Algorithms are a naturally useful framework for self-adaptation and that the use of a cultural framework to support self-adaptation in Evolutionary Programming can produce substantial performance improvements over population-only systems as expressed in terms of (1) systems success ratio, (2) execution CPU time, and (3) convergence (mean best solution) for a given set of 34 function minimization problems. The nature of these improvements and the type of knowledge that is most effective in producing them depend on the problem's functional landscape. In addition, it was found that the same held true for the population-only self-adaptive EP systems. Each level of self-adaptation (component, individual, and population) outperformed the others for problems with particular landscape features.

This paper proposes a means for schema versioning in a temporal, object-oriented data model called TOODM. A data definition language (DDL) is developed to describe evolving schema definitions. This DDL incorporates the concept of time sequences to store histories of a schema's properties. Update and retrieval operators for the manipulation of the schema are also developed. The data model has been implemented as a graphical front-end to POSTGRES.

In order to support uniform access to heterogeneous temporal information, we introduce the concept of a temporal mediator. A temporal mediator consists of three components: (i) a repository for windowing functions and conversion functions, (ii) a time granularity thesaurus and (iii) a query interpreter. There are two types of windowing functions: one associates each time point to a set of tuples, and the other associates each tuple to a set of time points. A conversion function transforms information in terms of one time granularity into that in terms of another time granularity. The time granularity thesaurus stores the knowledge about time granularities (e.g., names of time granularities and relationships among them). Users pose queries using the windowing functions and in terms of desired time granularities. (A query language, which can be used to form such queries, is given in the paper.) To answer such a user query, the query interpreter first employs the windowing functions together with the time granularity thesaurus to retrieve needed temporal data from the underlying databases and then uses the time granularity thesaurus to select suitable conversion functions which convert the responses to the desired time granularities. Thus, a temporal mediator provides a simple interface that supports uniform accesses to heterogeneous temporal databases.

There has been a considerable amount of work on object-oriented databases, active databases, and deductive databases. The common objective of these efforts is to produce highly intelligent and active systems for supporting the next generation of database applications. These future systems must be capable of capturing the concepts of time and managing not just temporal data but temporal knowledge expressed by knowledge rules. In this paper, we describe our efforts on a temporal object-oriented knowledge model, OSAM*/T, its associated temporal query language, OQL/T, an underlying temporal algebra, TA-algebra, and some implementation techniques. In addition to the features of the traditional object-oriented paradigm, the model is characterized by its strong support of association types and its incorporation of temporal knowledge rules for specifying temporal and other types of semantic constraints associated with object classes and their temporal object instances. The query language is featured by its pattern-based specification of temporal object associations, which allows complex queries with various time constraints to be formulated in a relatively simple way. The temporal algebra provides a set of primitive operators for manipulating homogeneous and/or heterogeneous patterns of temporal object associations, thus providing the needed mathematical foundation for processing and optimizing temporal queries. The implementation techniques include a Delta-Instance and Multi-Snapshot Storage Model, as well as data partitioning and clustering schemes for storage management of temporal knowledge bases.

A study of the function approximation capabilities of single hidden layer neural networks strongly motivates the investigation of constructive learning techniques as a means of realizing established error bounds. Learning characteristics employed by constructive algorithms provide ideas for development of new algorithms applicable to the function approximation problem. In addition, constructive techniques offer efficient methods for network construction and weight determination. The development of a novel neural network algorithm, the Constructive Locally Fit Sigmoids (CLFS) function approximation algorithm, is presented in detail. Basis functions of global extent (piecewise linear sigmoidal functions) are locally fit to the target function, resulting in a pool of candidate hidden layer nodes from which a function approximation is obtained. This algorithm provides a methodology of selecting nodes in a meaningful way from the infinite set of possibilities and synthesizes an n node single hidden layer network with empirical and analytical results that strongly indicate an O(1/n) mean squared training error bound under certain assumptions. The algorithm operates in polynomial time in the number of network nodes and the input dimension. Empirical results demonstrate its effectiveness on several multidimensional function approximate problems relative to contemporary constructive and nonconstructive algorithms.