Search results

Maturana and Mpodozis (2000) developed a theory of evolution that is based on the concept of autopoiesis and differs paradigmatically from the conventional theory derived from Darwin (1859). The present study aims to show that the authors have not exhausted the explanatory potential that the concept of autopoiesis can offer for the theory of evolution. Based on the critique of Maturana and Mpodozis, a system theoretic-oriented concept for the origin of species will be developed.

To render the explanatory potential of the concept of autopoiesis more fruitful for the theory of evolution, the proposition is made that the application of this concept is not limited to the molecular, or organismal level, as propounded by Maturana and Mpodozis, but should be also related to populations and species. By exempting the design of Maturana and Mpodozis from the rudiments of methodological individualism, a new field of application for the concept of autopoiesis is explored.

The proposed system theoretic concept of evolution theory makes it possible to shed new, constructive light on fundamental problems in the conventional biology of evolution. For example, with regard to the significance of the emergence of sexuality, or how phases of accelerated change in the course of evolution (e.g. the Cambrian explosion) are possible, or regarding the problem of the units of selection.

Although there have been attempts in the social sciences to interpret populations as autopoietic systems (for example by Niklas Luhmann), the proposed approach to evolutionary biology is new. Also original is a system theoretic conception of the evolutionary theory, in a strict renunciation of methodological individualism. This renunciation permits systems theories of evolution in social science and biology to be compared across disciplines.

The purpose of this paper is to establish a version of a theorem that originated from population genetics and has been later adopted in evolutionary computation theory that will lead to novel Monte‐Carlo sampling algorithms that provably increase the AI potential.

In the current paper the authors set up a mathematical framework, state and prove a version of a Geiringer‐like theorem that is very well‐suited for the development of Mote‐Carlo sampling algorithms to cope with randomness and incomplete information to make decisions.

This work establishes an important theoretical link between classical population genetics, evolutionary computation theory and model free reinforcement learning methodology. Not only may the theory explain the success of the currently existing Monte‐Carlo tree sampling methodology, but it also leads to the development of novel Monte‐Carlo sampling techniques guided by rigorous mathematical foundation.

The theoretical foundations established in the current work provide guidance for the design of powerful Monte‐Carlo sampling algorithms in model free reinforcement learning, to tackle numerous problems in computational intelligence.

Establishing a Geiringer‐like theorem with non‐homologous recombination was a long‐standing open problem in evolutionary computation theory. Apart from overcoming this challenge, in a mathematically elegant fashion and establishing a rather general and powerful version of the theorem, this work leads directly to the development of novel provably powerful algorithms for decision making in the environment involving randomness, hidden or incomplete information.

Dynamics of population mutagenesis during 22 consecutive generations of animals, as well as genetic radioadaptation were studied in natural populations of small mammals (bank voles) under chronic low‐intensive irradiation due to the Chernobyl accident. The data obtained point to oppositely directed processes in irradiated populations: accumulation of mutations (genetic load of populations) and formation of genetic radioadaptation. It is suggested that the frequencies of genetic damages in populations could be higher in the absence of radioadaptation process. A relationship between the frequencies of cytogenetic injuries and low doses of radiation was revealed in animal generations studied. The non‐linear dose‐effect curves are most likely to be defined by the complicated microevolutionary processes in populations. The results obtained indicate the absence of genetic effect threshold of low dose radiation. Besides, they show that a dependence of cytogenetic effects on radiation low doses in series of irradiated generations cannot be revealed using linear equations.

The purpose of this paper is to investigate the role of instruments defined as artefacts, rules, models or norms, in the articulation between knowing‐in‐practice and knowledge, in learning processes.

The paper focuses on a distributed, knowledge‐intensive and instrumented activity at the core of any collective action: qualification. The particular case of breeding activities in the livestock sector has been studied, where collective practices of animal qualification for collective breeding have been studied. Qualitative data stemming from in‐depth interviews and observation of daily practices have been analysed, combining practice‐based approaches on knowing processes and science philosophers' theories on the use of instruments during action.

The study of instruments used in daily practices allows us to go beyond the dichotomy between opposite types of knowledge, i.e. scientific knowledge seen as a stock, and sensible knowledge seen as purely tacit and equated to non‐instrumented practices. Instruments are not merely mediators in learning processes; they also take an active part in shaping and activating knowledge and learning processes.

Further research is needed on the designing of reflexive instrumentation, which takes knowing and knowledge articulation into account better.

Using instruments as a key concept to analyse knowing‐in‐practice processes has both methodological and managerial implications for identifying those instruments that favour learning processes.

This paper complements more classical practice‐based approaches by proposing a new perspective on instruments in learning processes, which is particularly relevant to the study of pluralistic organisations where power is diffuse.

In the Fall semester of 2001, a new position – Bioinformatics Librarian – was developed jointly by the University of Florida's Health Science Center Libraries (HSCL) and Genetics Institute (UFGI). Aims to give an overview of this post.

This paper describes the development of the position and the services provided.

Funded by the Genetics Institute and housed in the library, this position was created to meet the information needs of the university's faculty, students and staff involved in genetics and bioinformatics research and study. The responsibilities of the position were in part patterned after those performed via the HSCL's existing Liaison Librarian program. Librarians with only an undergraduate degree in the biosciences can still make an important, albeit usually less complete, contribution in this area.

The University of Florida's Bioinformatics Librarian position may serve as a model for the Information Specialist in Context (ISIC; Informationist; Bioinformationist) in the research arena.

Presents an overview of the field of genetic algorithms, pioneered in the field of natural adaptive systems and simulated in software. They are shown as representing a novel optimization strategy which is receiving much attention. In machine learning they are a component of classifier systems which are able to extract rules from data. The algorithms discussed are based on the principles of population genetics and biology.

The purpose of this paper is to use He's Exp‐function method (EFM) to construct solitary and soliton solutions of the nonlinear evolution equation.

This technique is straightforward and simple to use and is a powerful method to overcome some difficulties in the nonlinear problems.

This method is developed for searching exact traveling wave solutions of the nonlinear partial differential equations. The EFM presents a wider applicability for handling nonlinear wave equations.

The paper shows that EFM, with the help of symbolic computation, provides a straightforward and powerful mathematical tool for solving nonlinear evolution equations. Application of EFM to Fitzhugh‐Nagumo equation illustrates its effectiveness.

The purpose of this paper is to demonstrate a novel use of a generalized Lennard‐Jones (LJ) force law in Physicomimetics, combined with offline evolutionary learning, for the control of swarms of robots moving through obstacle fields towards a goal. The paper then extends the paradigm to demonstrate the utility of a real‐time online adaptive approach named distributed agent evolution with dynamic adaptation to local unexpected scenarios (DAEDALUS).

To achieve the best performance, the parameters of the force law used in the Physicomimetics approach are optimized, using an evolutionary algorithm (EA) (offline learning). A weighted fitness function is utilized consisting of three components: a penalty for collisions, lack of swarm cohesion, and robots not reaching the goal. Each robot of the swarm is then given a slightly mutated copy of the optimized force law rule set found with offline learning and the robots are introduced to a more difficult environment. The online learning framework (DAEDALUS) is used for swarm adaptation in this more difficult environment.

The novel use of the generalized LJ force law combined with an EA surpasses the prior state‐of‐the‐art in the control of swarms of robots moving through obstacle fields. In addition, the DAEDALUS framework allows the swarms of robots to not only learn and share behavioral rules in changing environments (in real time), but also to learn the proper amount of behavioral exploration that is appropriate.

There are significant issues that arise with respect to “wall following methods” and “local minimum trap” problems. “Local minimum trap” problems have been observed in this paper, but this issue is not addressed in detail. The intention is to explore other approaches to develop more robust adaptive algorithms for online learning. It is believed that the learning of the proper amount of behavioral exploration can be accelerated.

In order to provide meaningful comparisons, this paper provides a more complete set of metrics than prior papers in this area. The paper examines the number of collisions between robots and obstacles, the distribution in time of the number of robots that reach the goal, and the connectivity of the formation as it moves.

This paper addresses the difficult task of moving a large number of robots in formation through a large number of obstacles. The important real‐world constraint of “obstructed perception” is modeled. The obstacle density is approximately three times the norm in the literature. The paper shows how concepts from population genetics can be used with swarms of agents to provide fast online adaptive learning in these challenging environments. In addition, this paper also presents a more complete set of metrics of performance.