Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
In this paper, evolution strategies (ESs) — a class of evolutionary algorithms using normally distributed mutations, recombination, deterministic selection of the μ>1 best offspring individuals, and the principle of self-adaptation for the collective on-line learning of strategy parameters — are described by demonstrating their differences to genetic algorithms. By comparison of the algorithms,...
Evolutionary programming is a method for simulating evolution that has been investigated for over 30 years. This paper offers an introduction to evolutionary programming, and indicates its relationship to other methods of evolutionary computation, specifically genetic algorithms and evolution strategies. The original efforts that evolved finite state machines for predicting arbitrary time series,...
The Freidlin-Wentzell theory deals with the study of random perturbations of dynamical systems. We build several models of genetic algorithms by randomly perturbing simple processes. The asymptotic dynamics of the resulting processes is analyzed with the powerful tools developed by Freidlin and Wentzell and later by Azencott, Catoni and Trouvé in the framework of the generalized simulated annealing...
According to the literature that deals with the difficulty of functions with respect to genetic algorithms (GA), the so-called GA-hard functions are usually hard for other methods. In this paper, we firstly show that a gradient easy function can be fully deceptive, and thus hard for a GA to optimize while being unimodal. More generally, we show that the global search method introduced by (Das and...
This paper introduces a niching technique called GAS (S stands for species) which dinamically creates a subpopulation structure (taxonomic chart) using a radius function instead of a single radius, and a ‘cooling’ method similar to simulated annealing. GAS offers a solution to the niche radius problem with the help of these techniques. A method based on the speed of species is presented for determining...
During the last years, heterogeneous computing became more and more popular. Its advantage is availability, since any network of workstations can be used as a parallel virtual machine. On the other hand, programming these machines efficiently is even more difficult than programming a parallel dedicated machine. Each machine in the network may dynamically change performance, so optimal load balancing...
This paper presents a Machine Learning approach to control genetic algorithms. From examples gathered through spying evolution or experimenting on populations, induction extracts a rule-based characterization of which evolutionary events are good or bad for evolution. Such rule base allows for further generations to escape most disruptive or unproductive changes, according to a civilized rather than...
The design of a new product or of its manufacturing process consists in reconciling multiple objectives with each other to take into account their different features. In this paper, a new multicriteria optimization algorithm is presented. This method is based on the use of (i) a genetic algorithm (GA) which optimizes each system response and (ii) a selection algorithm which sorts Pareto-efficient...
In this paper, we study the coevolution of species by combining a theoretical approach with a computer simulation in order to show how a discrete distribution of viable species emerges. Coevolution is modelled as a replicator system which, with an additional diffusion term representing the mutation, leads to a Schrödinger equation. This system dynamics can be interpreted as a survival race between...
This article describes a new problem solving paradigm: the Symbiotic Algorithm. Problem solutions are considered as embedded organisms whose genetic materials are expressed as evaluable phenotypes. This organic hierarchy can be viewed as a biosphere containing the whole set of creatures manipulated by the algorithm. Being immortal, organisms do not replicate. The only changes occuring in the biosphere...
We propose here a new approach to study co-evolution and we apply it to the well-known iterated prisoner's dilemma. The originality of our work is that it uses a simplified version of the game, and thus, restrict the search space of evolutionary dynamics. This allows to have a look at the totality of the search space in permanence, and so, a complete understanding of the phenomenon of co-evolution...
Immune system (IS) is capable of evolving, learning, recognising and eliminating foreign molecules which invade organisms. Fault tolerant approaches consist in detecting erroneous states of an algorithm and executing recovery procedures. They are generally implemented by adding formal properties to be satisfied by state variables during execution. However, defining fault-tolerant procedures can be...
This paper illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex Artificial Neural Networks (ANN). Artificial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnetworks. Genetic programming is used to evolve coded grammars that generates ANNs for controlling a six-legged...
This paper describes an evolutionary process producing dynamical neural networks used as “brains” for autonomous agents. The main concepts used: genetic algorithms, morphogenesis process, artificial neural networks and artificial metabolism, illustrate our conviction that some fundamental principles of nature may help to design processes from which emerge artificial autonomous agents. The evolutionary...
This paper deals with technical issues relevant to artificial neural net (ANN) training by genetic algorithms. Neural nets have applications ranging from perception to control; in the context of control, achieving great precision is more critical than in pattern recognition or classification tasks. In previous work, the authors have found that when employing genetic search to train a net, both precision...
We address here the resolution of the so-called inverse problem for IFS. This problem has already been widely considered, and some studies have been performed for affine IFS, using deterministic or stochastic methods (simulated annealing or Genetic Algorithm) [9, 12, 6]. When dealing with non affine IFS, the usual techniques do not perform well, except if some a priori hypotheses on the structure...
Evolutionary algorithms of selection and variation by recombination and/or mutation have been used to simulate biological evolution. This paper demonstrates how interactive evolution can be used to study the evolution of simulated natural evolution. Since interactive evolution allows the user to direct the development of models of natural systems, it can be used to direct the evolution of models of...
Particle-based models and articulated models are increasingly used in synthetic image animation applications. This paper aims at showing examples of how Evolutionary Algorithms can be used as tools to build realistic physical models for image animation. First, a method to detect regions with rigid 2D motion in image sequences, without solving explicitly the Optical Flow equation, is presented...
We investigate the use of genetic algorithms (GAs) in the framework of image primitives extraction (such as segments, circles, ellipses or quadrilaterals). This approach completes the well-known Hough Transform, in the sense that GAs are efficient when the Hough approach becomes too expensive in memory, i.e. when we search for complex primitives having more than 3 or 4 parameters. Indeed, a...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.