A Prolog implementation of a simple Lamarckian evolution module for genetic algorithms is discussed. Lamarckian evolution posits that characteristics acquired during a phenotype's lifetime may be inherited by offspring. Although largely dismissed as a viable evolutionary theory for natural systems, Lamarckian evolution has proven effective within computer applications. The strengths of the impl...

This paper discusses schema evolution in software engineering databases. After a study of existing approaches, we show that these approaches do not satisfy software engineering requirements. Then, we present our model, which supports multiple schema compositions and multiple evolution policies, each application being free to define its evolution strategy. Management of our system is based on cl...

The paper discusses three major issues First it discusses why it makes sense to approach problems in a hierarchical fashion It de nes the class of hierarchically decomposable functions that can be used to test the algorithms that approach problems in this fashion Finally the Bayesian optimization algorithm BOA is extended in order to solve the proposed class of problems

OF THE DISSERTATION Composition of Image Analysis Processes through Object-Centered Hierarchical Planning by Leiguang Gong, Ph.D. Dissertation Director: Professor Casimir A. Kulikowski The present thesis describes a new approach to biomedical image interpretation | the knowledge-based composition of image analysis processes through object-centered hierarchical planning. Its computer implementat...

In this paper a new method for defining a transformation between a source color space and a more perceptual uniform color space will be presented. The main idea is to use a three dimensional FreeForm Deformation to deform a source color space in such a way, that the new distances between chosen color samples match psychophysically estimated data as close as possible. This deformation of space i...

Comparable to other optimization techniques, the performance of Evolution Strategies (ESs) depends on a suitable choice of internal strategy control parameters. Apart from a xed setting, ESs facilitate an adjustment of such parameters within a selfadaptation process. For step-size control in particular, various adaptation concepts have been evolved early in the development of ESs. These algorit...

Mutagenesis is a process which forces the coverage of certain zones of the search space during the generations of an evolution strategy, by keeping track of the covered ranges for the different variables in the so called gene matrix. Originally introduced as an artifact to control the automated stopping criterion in a memetic algorithm, ESLAT, it also improved the exploration capabilities of th...

Case-based problem-solving systems reason and learn from experiences, building up case libraries of problems and solutions to guide future reasoning. The expected bene ts of this learning process depend on two types of regularity: (1) problem-solution regularity, the relationship between problem-to-problem and solution-to-solution similarity measures that assures that solutions to similar prior...

A framework for knowledge-based scienti c discovery in geological databases has been developed. The discovery process consists of two main steps: context de nition and equation derivation. Context de nition properly de nes and formulates homogeneous regions, each of which is likely to produce a unique and meaningful analytic formula for the goal variable. Clustering techniques and a suite of vi...

Due to the exibility in adapting to diierent tness landscapes, self-adaptive evolutionary algorithms (SA-EAs) have been gaining popularity in the recent past. In this paper, we postulate the properties that SA-EA operators should have for successful applications in real-valued search spaces. Speciically, population mean and variance of a number of SA-EA operators, such as various real-parameter...