The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper reports a comparison of several bloat control methods and also evaluates a new proposal for limiting the size of the individuals: a genetic operator called prune and plant. The aim of this work is to prove the adequacy of this new method. Since a preliminary study of the method has already shown promising results, we have performed a thorough study in a set of benchmark problems aiming...
Genetic network programming (GNP), one of the extended evolutionary algorithms was proposed, whose gene is constructed by the directed graph. GNP can perform a global searching, but it lacks of the exploitation ability. Since the behavior of GNP is characterized by the balance between exploitation and exploration in the search space, we proposed a hybrid algorithm in this paper that combines GNP with...
In this paper we propose a multi-objective genetic algorithm to generate Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we exploit a chromosome composed of two parts, which codify...
Hybridizing of evolutionary algorithms (EA) by means of local search has shown considerable performance improvement in single-objective optimization (SOO) field. The fine search in the neighborhood of the EA individuals (solutions) allows a fine exploration of the solution space. This paper investigates the application and the evaluation of the hybridizing mechanism of the EAs in the multi-objective...
The requirements for quality of service (QoS) of applications in networks are becoming stricter. Toward to solve the routing problem in IP networks, MPLS architecture allows to define explicit routes in the network. This paper describes a multi-objective heuristic approach to solve the routing problem in MPLS networks. An evolutionary multi-objective algorithm is proposed based on Dijkstra's shortest...
Radial basis function networks (RBFNs) have shown their capability to be used in classification problems, so that many data mining algorithms have been developed to configure RBFNs. These algorithms need to be given a suitable set of parameters for every problem they face, thus methods to automatically search the values of these parameters are required. This paper shows the robustness of a meta-algorithm...
Most symbolic classifiers aim at building sets of rules with good coverage and precision. While this is suitable for most applications, they tend to neglect other desirable properties, such as the ability to induce novel knowledge or to show new points of view of well-established concepts. An approach to overcome these limitations involves using a multi-objective evolutionary algorithm to build knowledge...
Learning classifier systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. The new model of LCS - so-called rGCS - is used to classify real-valued data. In order to handle...
This paper proposes an algorithm to solve multi-objective problems by Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) which uses an external population, called pareto vector set P, in genetic operators. RasID is an optimization algorithm, which is good at finding local optima, but its diversified search isn't so efficient. To increase its...
We report a detailed analysis on the effect of structural elitism in the global minimum search of atomic clusters by a hybrid evolutionary algorithm. Structural elitism allows to preserve a certain diversity in the population and is based on the classification of clusters as spheric, oblate, prolate or asymmetric according to their shape. The search algorithm has been applied to perform the global...
The goal of this paper is twofold. First, we want to make a study about how evolutionary computation techniques can efficiently solve the radio network design problem. For this goal we test several evolutionary computation techniques within the OPLINK experimental framework and compare them. Second, we propose a clustering approach and a 2-OPT in order to improve the results obtained by the evolutionary...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.