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Evolutionary Technique (evolutionary + technique)

Distribution by Scientific Domains

Engineering	75%

Selected Abstracts

ON SOCIAL LEARNING AND ROBUST EVOLUTIONARY ALGORITHM DESIGN IN THE COURNOT OLIGOPOLY GAME

COMPUTATIONAL INTELLIGENCE, Issue 2 2007
Floortje Alkemade
Agent-based computational economics (ACE) combines elements from economics and computer science. In this article, the focus is on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the genetic algorithm directly from the values of the economic model parameters. This article compares two important approaches that are dominating in ACE and shows that the above practice may hinder the performance of the genetic algorithm and thereby hinder agent learning. More specifically, it is shown that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE. [source]

NETCAP: a capacity planning tool for practical content distribution network designs

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 2 2007
Sami J. Habib
Abstract This paper describes a capacity planning tool NETCAP, which is a prototype software program for automatically planning and integrating application-specific content-distribution networks (CDNs). The CDN integration problem consists of two problems: data management system design problem and network topology design problem. The data management system design problem comprises of the server placement and file allocation problems, where the network topology design problem involves determining the network topology with network technology considerations. The CDN integration problem has been formulated as an optimization problem; where the objective function is to optimize a network topology that satisfies both the servers' access requirements and clients' communications. An evolutionary technique is used in NETCAP to search the design space. The experimental results for a CDN integration problem described here demonstrate the effectiveness of NETCAP in finding good CDN designs from a large design space in a few minutes. Copyright © 2006 John Wiley & Sons, Ltd. [source]

GENETIC PROGRAMMING AND ITS APPLICATION IN REAL-TIME RUNOFF FORECASTING,

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2001
Soon Thiam Khu
ABSTRACT: Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real-time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall-runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real-time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly. [source]

Dielectric filter optimal design suitable for microwave communications by using multiobjective evolutionary algorithms

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 10 2007
S. K. Goudos
Abstract A multiobjective evolutionary technique is applied to design dielectric filters useful in microwave communications technology. The optimal geometry of the filters is derived by utilizing two different multiobjective optimization algorithms. The first one is the Nondominated Sorting Genetic Algorithm-II (NSGA-II), which is a popular multiobjective genetic algorithm. The second algorithm is based on multiobjective Particle Swarm Optimization with fitness sharing (MOPSO-fs). MOPSO-fs algorithm is a novel Pareto PSO algorithm that produces the Pareto front in a fast and efficient way. In the present work, MOPSO-fs is compared with NSGA-II to optimize the geometry of the filters under specific requirements concerning the frequency response of the filters. Several examples are studied to exhibit the efficiency of the multiobjective evolutionary optimizers and also the ability of the technique to derive optimal structures that can be used in practice. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2324,2329, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22755 [source]