12195414
Evolving Controllers for Autonomous Agents Using Genetically Programmed Networks
This article presents a new approach to the evolution of controllers for autonomous agents. We propose the evolution of a connectionist structure where each node has an associated program, evolved using genetic programming. We call this structure a Genetically Programmed Network and use it to successfully evolve control systems with very different architectures, by making small restrictions to the evolutionary process. Experimental results of applying this method to evolve neural networks, distributed programs and rule-based systems capable of solving a common benchmark problem, the Ant Problem, are presented. Comparison with other known genetic programming based approaches, shows that our method requires less effort to find a solution.
2626702
A Particle Swarm Model of Organizational Adaptation
This study introduces the particle swarm metaphor to the domain of organizational adaptation. A simulation model (OrgSwarm) is constructed to examine the impact of strategic inertia, in the presence of errorful assessments of future payoffs to potential strategies, on the adaptation of the strategic fitness of a population of organizations. The results indicate that agent (organization) uncertainty as to the payoffs of potential strategies has the affect of lowering average payoffs obtained by a population of organizations. The results also indicate that a degree of strategic inertia, in the presence of an election mechanism, assists rather than hampers adaptive efforts in static and slowly changing strategic environments.
5929323
Agent-based modelling of product invention
This study describes a novel simulation model of the process of product invention. Invention is conceptualized as a process of directed evolutionary adaptation, on a landscape of product design possibilities, by a population of profit-seeking agents (inventors). The simulation experiments examine the sensitivity of the rate of advance in product fitness to the choice of search heuristics employed by inventors. The key finding of the experiments is that if search heuristics are confined to those which are rooted in past experience, or to heuristics which merely generate variety, limited product advance occurs. Notable product fitness advance only occurs when inventor's expectations as to the relative fitness of potential product inventions are incorporated into the model of invention. The results demonstrate the importance of human direction and expectations in invention. They also support the importance of formal product / project evaluation procedures in organizations, and the importance of market information when inventing new products.
18811835
An Empirical Comparison of Particle Swarm and Predator Prey Optimisation
In this paper we present and discuss the results of experimentally comparing the performance of several variants of the standard swarm particle optimiser and a new approach to swarm based optimisation. The new algorithm, which we call predator prey optimiser, combines the ideas of particle swarm optimisation with a predator prey inspired strategy, which is used to maintain diversity in the swarm and preventing premature convergence to local suboptima. This algorithm and the most common variants of the particle swarm optimisers are tested in a set of multimodal functions commonly used as benchmark optimisation problems in evolutionary computation.
7409552
Swarm optimisation as a new tool for data mining
This paper proposes the use of particle swarm optimisers as a tool for data mining. To evaluate its usefulness, we empirically compare the performance of three variants of the particle optimiser with another evolutionary algorithm, namely a genetic algorithm, in rule discovery for classification tasks. Such tasks are considered core tools for decision support systems in a widespread area, ranging from the industry, commerce, military and scientific fields. The data sources used here for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that particle swarm optimisers are competitive with other evolutionary techniques, and can be successfully applied to more demanding problem domains.
6828563
A Particle Swarm Data Miner
This paper describes the implementation of Data Mining tasks using Particle Swarm Optimisers. The object of our research has been to apply such algorithms to classification rule discovery. Results, concerning accuracy and speed performance, were empirically compared with another evolutionary algorithm, namely a Genetic Algorithm and with J48 – a Java implementation of C4.5. The data sets used for experimental testing have already been widely used and proven reliable for testing other Data Mining algorithms. The obtained results seem to indicate that Particle Swarm Optimisers are competitive with other evolutionary techniques, and could come to be successfully applied to more demanding problem domains.
30725726
An integrated MOLP method base package - A guided tour of tommix
Abstract An implementation of a MOLP interactive method base is presented in the form of a guided tour to a computer package. The MOLP method base (associated to a procedure toolbox) is a component of a decision support system framework which also integrates a dialogue base and a data management module. Besides providing the standard working of each method the interactive MOLP method base offers the possibility of method switching at any interaction with the decision maker (DM), in order to make the most of the potentialities of each method and support a “learning” process. Some key issues in the development of interactive MOLP approaches and computational tools are also discussed. Special attention is paid to the development of an user-friendly environment for interacting with the DM.
9321144
Using separation and composition of concerns to build multiuser virtual environments
Developing multiuser virtual environments (MUVE) is a very complex task since it involves several engineering domain aspects such as virtual reality, cooperative work and distributed systems. Furthermore, existing technologies like graphics capabilities, processing power and network bandwidth, are permanently evolving. For MUVE systems to be successfully developed, they must not only deal with all the different aspects that are inherent to these systems but also have the ability to deal with the continuous technology and requirements evolution. To cope with these problems, a software engineering separation of concerns approach is proposed. Concerns are identified for each of the different aspects of MUVEs. To obtain the necessary flexibility, each one of the concerns should abstract its possible variations in order to support the future system evolution. Moreover it is shown how concerns composition has the necessary expressive power to build multiuser virtual environments.
18125589
Zerg: A Very Fast BLAST Parser Library
SUMMARY
Zerg is a library of sub-routines that parses the output from all NCBI BLAST programs (Blastn, Blastp, Blastx, Tblastn and Tblastx) and returns the attributes of a BLAST report to the user. It is optimized for speed, being especially useful for large-scale genomic analysis. Benchmark tests show that Zerg is over two orders of magnitude faster than some widely used BLAST parsers.


AVAILABILITY
http://bioinfo.iq.usp.br/zerg
12508242
MEGICO: An Intelligent Knowledge Management Methodology
In this paper, we describe a knowledge management system (KMS) design and construction methodology. For this purpose, section 1 gives a brief introduction that justifies the need for a genuine KMS methodology, like MEGICO. Section 2 very briefly presents MEGICO and section 3 discusses the results and conclusions of its use.
7316091
SAM Method as an Approach to Select Candidates for Human Prostate Cancer Markers
In order to select gene markers among differentially expressed transcripts identified from tumoral prostate, we have applied a filter and Significance Analysis of Microarrays (SAM) as the feature selection method on a previously normalized dataset of DNA microarray experiments reported by Reis et al., 2004 (Oncogene 23:6684-6692). Twenty seven samples with different degrees of tumor differentiation (Gleason scores) were analyzed. SAM was run using either two-class, unpaired data analysis with Gleason 5-6 and Gleason 9-10 samples, or multiclass response analysis with an additional category of Gleason 7-8. Both strategies revealed a promising set of transcripts associated with the degree of differentiation of prostate tumors.