Jacobi script v sign-functions and discrete Fourier transforms

Properties of the Jacobi script v sign3-function and its derivatives under discrete Fourier transforms are investigated, and several interesting results are obtained. The role of modulo N equivalence classes in the theory of script v sign-functions is stressed. An important conjecture is studied. © 2006 American Institute of Physics.

Probabilidade de emergência de classes ordinais após o ensino de relações numéricas

O paradigma de equivalência de estímulos tem se mostrado útil na explicação de processos comportamentais complexos, como aqueles envolvidos em comportamentos conceituais numéricos. Vários estudos têm buscado a compreensão de como desempenhos sob controle da função de ordem são estabelecidos e mantidos. O objetivo do presente trabalho foi verificar se classes ordinais poderiam emergir após o ensino por emparelhamento arbitrário e de produção de seqüência. Participaram do estudo três alunos com atraso no desenvolvimento. Os estímulos visuais foram formas abstratas indicando numerosidade (A), numerais cardinais (B)e nomes escritos em letras maiúsculas de numerais (C). As sessões experimentais foram conduzidas em uma sala da APAE-BELÉM e um software controlou e registrou os dados comportamentais. As relações AB/AC foram ensinadas e testou-se a emergência de três classes de equivalência. Em seguida, houve um ensino por encadeamento de respostas com estímulos de um dos conjuntos (A1A2A3) e uma sonda de seqüenciação. Então, foi avaliada a emergência de novas seqüências (B1B2B3 e C1C2C3). Posteriormente, testes de substitutabilidade foram aplicados para verificar a formação de classes ordinais (por exemplo: A1B2C3). Testes de generalização também foram apresentados para verificar se um responder envolvendo numerosidade ocorreria com novos estímulos (por exemplo: E1E2E3). Os resultados demonstraram que os participantes responderam a novas seqüências prontamente ou com emergência gradual. A análise de topografias de controle de estímulos envolvidas nesse tipo de tarefa mostrou-se útil para a compreensão da ordinalidade. Todos os participantes responderam a seqüências com novos estímulos (generalização). O procedimento mostrou-se também eficiente na transferência de funções ordinais em pessoas com atraso no desenvolvimento.

Hyperspace geography: Visualizing fitness landscapes beyond 4D

Human perception is finely tuned to extract structure about the 4D world of time and space as well as properties such as color and texture. Developing intuitions about spatial structure beyond 4D requires exploiting other perceptual and cognitive abilities. One of the most natural ways to explore complex spaces is for a user to actively navigate through them, using local explorations and global summaries to develop intuitions about structure, and then testing the developing ideas by further exploration. This article provides a brief overview of a technique for visualizing surfaces defined over moderate-dimensional binary spaces, by recursively unfolding them onto a 2D hypergraph. We briefly summarize the uses of a freely available Web-based visualization tool, Hyperspace Graph Paper (HSGP), for exploring fitness landscapes and search algorithms in evolutionary computation. HSGP provides a way for a user to actively explore a landscape, from simple tasks such as mapping the neighborhood structure of different points, to seeing global properties such as the size and distribution of basins of attraction or how different search algorithms interact with landscape structure. It has been most useful for exploring recursive and repetitive landscapes, and its strength is that it allows intuitions to be developed through active navigation by the user, and exploits the visual system's ability to detect pattern and texture. The technique is most effective when applied to continuous functions over Boolean variables using 4 to 16 dimensions.

On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator

In this paper, we address some issue related to evaluating and testing evolutionary algorithms. A landscape generator based on Gaussian functions is proposed for generating a variety of continuous landscapes as fitness functions. Through some initial experiments, we illustrate the usefulness of this landscape generator in testing evolutionary algorithms.

Finding the Relationship between a Search Algorithm and a Class of Functions on Discrete Space by Exhaustive Search

* The work is supported by RFBR, grant 04-01-00858-a.

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

ContinuousMultimodal Global Optimization with Differential Evolution-Based Methods

Metaheuristic methods have become increasingly popular approaches in solving global optimization problems. From a practical viewpoint, it is often desirable to perform multimodal optimization which, enables the search of more than one optimal solution to the task at hand. Population-based metaheuristic methods offer a natural basis for multimodal optimization. The topic has received increasing interest especially in the evolutionary computation community. Several niching approaches have been suggested to allow multimodal optimization using evolutionary algorithms. Most global optimization approaches, including metaheuristics, contain global and local search phases. The requirement to locate several optima sets additional requirements for the design of algorithms to be effective in both respects in the context of multimodal optimization. In this thesis, several different multimodal optimization algorithms are studied in regard to how their implementation in the global and local search phases affect their performance in different problems. The study concentrates especially on variations of the Differential Evolution algorithm and their capabilities in multimodal optimization. To separate the global and local search search phases, three multimodal optimization algorithms are proposed, two of which hybridize the Differential Evolution with a local search method. As the theoretical background behind the operation of metaheuristics is not generally thoroughly understood, the research relies heavily on experimental studies in finding out the properties of different approaches. To achieve reliable experimental information, the experimental environment must be carefully chosen to contain appropriate and adequately varying problems. The available selection of multimodal test problems is, however, rather limited, and no general framework exists. As a part of this thesis, such a framework for generating tunable test functions for evaluating different methods of multimodal optimization experimentally is provided and used for testing the algorithms. The results demonstrate that an efficient local phase is essential for creating efficient multimodal optimization algorithms. Adding a suitable global phase has the potential to boost the performance significantly, but the weak local phase may invalidate the advantages gained from the global phase.

Models to study intermediate filament dynamics and functions

Intermediate filaments are part of the cytoskeleton and nucleoskeleton; they provide cells with structure and have important roles in cell signalling. The IFs are a large protein family with more than 70 members; each tightly regulated and expressed in a cell type-specific manner. Although the IFs have been known and studied for decades, our knowledge about their specific functions is still limited, despite the fact that mutations in IF genes cause numerous severe human diseases. In this work, three IF proteins are examined more closely; the nuclear lamin A/C and the cytoplasmic nestin and vimentin. In particular the regulation of lamin A/C dynamics, the role of nestin in muscle and body homeostasis as well as the functions and evolutionary aspects of vimentin are investigated. Together this data highlights some less well understood functions of these IFs. We used mass-spectrometry to identify inter-phase specific phosphorylation sites on lamin A. With the use of genetically engineered lamin A protein in combination with high resolution microscopy and biochemical methods we discovered novel roles for this phosphorylation in regulation of lamin dynamics. More specifically, our data suggests that the phosphorylation of certain amino acids in lamin A determines the localization and dynamics of the protein. In addition, we present results demonstrating that lamin A regulates Cdk5-activity. In the second study we use mice lacking nestin to gain more knowledge of this seldom studied protein. Our results show that nestin is essential for muscle regeneration; mice lacking nestin recover more slowly from muscle injury and show signs of spontaneous muscle regeneration, indicating that their muscles are more sensitive to stresses and injury. The absence of nestin also leads to decreased over-all muscle mass and slower body growth. Furthermore, nestin has a role in controlling testicle homeostasis as nestin-/- male mice show a greater variation in testicle size. The common fruit fly Drosophila melanogaster lacks cytoplasmic IFs as most insects do. By creating a fly that expresses human vimentin we establish a new research platform for vimentin studies, as well as provide a new tool for the studies of IF evolution.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Evolving Classifiers - Evolutionary Algorithms in Data Mining

Data mining means to summarize information from large amounts of raw data. It is one of the key technologies in many areas of economy, science, administration and the internet. In this report we introduce an approach for utilizing evolutionary algorithms to breed fuzzy classifier systems. This approach was exercised as part of a structured procedure by the students Achler, Göb and Voigtmann as contribution to the 2006 Data-Mining-Cup contest, yielding encouragingly positive results.

Dynamic Niche Clustering: a fuzzy variable radius niching technique for multimodal optimisation in GAs

This paper describes the recent developments and improvements made to the variable radius niching technique called Dynamic Niche Clustering (DNC). DNC is fitness sharing based technique that employs a separate population of overlapping fuzzy niches with independent radii which operate in the decoded parameter space, and are maintained alongside the normal GA population. We describe a speedup process that can be applied to the initial generation which greatly reduces the complexity of the initial stages. A split operator is also introduced that is designed to counteract the excessive growth of niches, and it is shown that this improves the overall robustness of the technique. Finally, the effect of local elitism is documented and compared to the performance of the basic DNC technique on a selection of 2D test functions. The paper is concluded with a view to future work to be undertaken on the technique.