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Wyszukujesz frazę "evolutionary" wg kryterium: Temat


Wyświetlanie 1-13 z 13
Tytuł:
Searching for optimal size neural networks in Assembler Encoding
Autorzy:
Praczyk, T.
Powiązania:
https://bibliotekanauki.pl/articles/970178.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
evolutionary neural networks
Opis:
Assembler Encoding represents a neural network in the form of a simple program called Assembler Encoding Program. The task of the program is to create the so-called Network Definition Matrix, which maintains all the information necessary to construct a network. To generate the programs and, in consequence, neural networks, evolutionary techniques are used. One of the problems in Assembler Encoding is to determine an optimal number of neurons in a neural network. To deal with this problem a current version of Assembler Encoding uses a solution that is time consuming and hence rather impractical. The paper proposes four other solutions to the problem mentioned. To test them, experiments in a predator-prey problem were carried out. The results of the experiments are included at the end of the paper.
Źródło:
Control and Cybernetics; 2010, 39, 4; 1193-1215
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary approach to obtain graph covering by densely connected subgraphs
Autorzy:
Stańczak, J.
Potrzebowski, H.
Sęp, K.
Powiązania:
https://bibliotekanauki.pl/articles/206170.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
graph
clique
graph clustering
evolutionary algorithms
Opis:
This article describes two evolutionary methods for dividing a graph into densely connected structures. The first method deals with the clustering problem, where the element order plays an important role. This formulation is very useful for a wide range of Decision Support System (DSS) applications. The proposed clustering method consists of two stages. The first is the stage of data matrix reorganization, using a specialized evolutionary algorithm. The second stage is the final clustering step and is performed using a simple clustering method (SCM). The second described method deals with a completely new partitioning algorithm, based on the subgraph structure we call α-clique. The α-clique is a generalization of the clique concept with the introduction of parameter α, which imposes for all vertices of the subgraph the minimal percentage (α*100%) of vertices of this subgraph that must be connected with vertices of this α-clique. Traditional clique is an instance of α-clique with α = 1. Application of this parameter makes it possible to control the degree (or strength) of connections among vertices (nodes) of this subgraph structure. The evolutionary approach is proposed as a method that enables finding separate α-cliques that cover the set of graph vertices.
Źródło:
Control and Cybernetics; 2011, 40, 3; 849-875
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using Assembler Encoding to build neuro-controllers for a team of autonomous underwater vehicles
Autorzy:
Praczyk, T.
Szymak, P.
Powiązania:
https://bibliotekanauki.pl/articles/206308.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
evolutionary neural networks
autonomous underwater vehicles
Opis:
The paper compares a neuro-evolutionary metod called Assembler Encoding with two other methods from the area of neuro–evolution. As a testbed for the methods a variant of the predator–prey problem with Autonomous Underwater Vehicles (AUV) operating in an environment with the sea current was used. In the experiments, the task of vehicles–predators controlled with evolutionary neural networks was to capture a vehicle–prey behaving according to a simple deterministic strategy. All the experiments were carried out in simulation, and in order to simplify calculations in the two–dimensional environment – AUVs moved on a horizontal surface under the water.
Źródło:
Control and Cybernetics; 2013, 42, 1; 267-286
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fundamentals of scatter search and path relinking
Autorzy:
Glover, F.
Laguna, M.
Marti, R.
Powiązania:
https://bibliotekanauki.pl/articles/205907.pdf
Data publikacji:
2000
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
optymalizacja
evolutionary methods
metaheuristics
optimization
tabu search
Opis:
The evolutionary approach called Scatter Search, and its generalized form called Path Relinking, have proved unusually effective for solving a diverse array of optimization problems from both classical and real world settings. Scatter Search and Path Relinking differ from other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on generalized path constructions (in both Euclidean and neighborhood spaces) and by utilizing strategic designs where other approaches resort to randomization. Scatter Search and Path Relinking are also intimately related to the Tabu Search metaheuristic, and derive additional advantages by making use of adaptive memory and associated memory-exploiting mechanisms that are capable of being adapted to particular contexts. We describe the features of Scatter Search and Path Relinking that set them apart from other evolutionary approaches, and that offer opportunities for creating increasingly more versatile and effective methods in the future.
Źródło:
Control and Cybernetics; 2000, 29, 3; 653-684
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary neural-networks based optimisation for short-term load forecasting
Autorzy:
Grzenda, M.
Macukow, B.
Powiązania:
https://bibliotekanauki.pl/articles/206850.pdf
Data publikacji:
2002
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
optymalizacja
programowanie ewolucyjne
sieć neuronowa
evolutionary programming
neural networks
optimisation
Opis:
The purpose of short-term load forecasting is to optimise the power supply volume in short time horizon. There is no straightforward mapping rule between the type of time period and the resulting power consumption. Still, it is inevitable for the overall efficiency of the power system to rely on a good prediction model. Our paper illustrates a novel approach based on evolutionary programming. Feedforward networks are being evolved by the ECoMLP method in order to properly solve the optimisation problem, defined as minimisation of the prediction error. All the results have been obtained using the data from the Polish Power System. The data used for the training and tests has been chosen so as to reflect both short-time and long-time dependencies between time period category and load of the system. The primary feature of the described method is a novel self-adaptive procedure that is a part of a sophisticated design algorithm serving to select both network architecture and weight connections. Due to the application of this procedure, no time consuming tests are required to train and retrain neural prediction models. Therefore, the method makes it possible to construct and maintain prediction models for load forecasting without expert knowledge about neural networks.
Źródło:
Control and Cybernetics; 2002, 31, 2; 371-382
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Learning board evaluation function for Othello by hybridizing coevolution with temporal difference learning
Autorzy:
Szubert, M.
Jaśkowski, W.
Krawiec, K.
Powiązania:
https://bibliotekanauki.pl/articles/206175.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
evolutionary computation
coevolutionary algorithms
reinforcement learning
memetic computing
game strategy learning
Opis:
Hybridization of global and local search techniques has already produced promising results in the fields of optimization and machine learning. It is commonly presumed that approaches employing this idea, like memetic algorithms combining evolutionary algorithms and local search, benefit from complementarity of constituent methods and maintain the right balance between exploration and exploitation of the search space. While such extensions of evolutionary algorithms have been intensively studied, hybrids of local search with coevolutionary algorithms have not received much attention. In this paper we attempt to fill this gap by presenting Coevolutionary Temporal Difference Learning (CTDL) that works by interlacing global search provided by competitive coevolution and local search by means of temporal difference learning. We verify CTDL by applying it to the board game of Othello, where it learns board evaluation functions represented by a linear architecture of weighted piece counter. The results of a computational experiment show CTDL superiority compared to coevolutionary algorithm and temporal difference learning alone, both in terms of performance of elaborated strategies and computational cost. To further exploit CTDL potential, we extend it by an archive that keeps track of selected well-performing solutions found so far and uses them to improve search convergence. The overall conclusion is that the fusion of various forms of coevolution with a gradient-based local search can be highly beneficial and deserves further study.
Źródło:
Control and Cybernetics; 2011, 40, 3; 805-831
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A survey of big data classification strategies
Autorzy:
Banchhor, Chitrakant
Srinivasu, N.
Powiązania:
https://bibliotekanauki.pl/articles/2050171.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
big data
data mining
MapReduce
classification
machine learning
evolutionary intelligence
deep learning
Opis:
Big data plays nowadays a major role in finance, industry, medicine, and various other fields. In this survey, 50 research papers are reviewed regarding different big data classification techniques presented and/or used in the respective studies. The classification techniques are categorized into machine learning, evolutionary intelligence, fuzzy-based approaches, deep learning and so on. The research gaps and the challenges of the big data classification, faced by the existing techniques are also listed and described, which should help the researchers in enhancing the effectiveness of their future works. The research papers are analyzed for different techniques with respect to software tools, datasets used, publication year, classification techniques, and the performance metrics. It can be concluded from the here presented survey that the most frequently used big data classification methods are based on the machine learning techniques and the apparently most commonly used dataset for big data classification is the UCI repository dataset. The most frequently used performance metrics are accuracy and execution time.
Źródło:
Control and Cybernetics; 2020, 49, 4; 447-469
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Properties of an α-clique approach to obtaining the hub and spoke structure in optimization of transportation systems
Autorzy:
Mazbic-Kulma, B.
Owsinski, J. W.
Barski, A.
Sęp, K.
Stańczak, J.
Powiązania:
https://bibliotekanauki.pl/articles/205710.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
transport
transport systems
graphs
hub and spoke
evolutionary algorithm
time-wise profitability
Opis:
The paper is devoted to the analysis of a graph transformation, pertinent for the transport and logistic systems and their planning and management. Namely, we consider, for a given graph, representing some existing transport or logistic system, its transformation to a (non-equivalent) so-called ”hub-and-spoke” structure, known from both literature and practice of transportation and logistics. This structure is supposed to bring benefits in terms of functioning and economic performance of the respective systems. The transformation into the ”hub-and-spoke” is not only non-equivalent (regarding the original graph of the system), but is also, in general, non unique. The structure sought is composed of two kinds of elements - nodes of the graph (stations, airports, havens, etc.), namely: the subgraph of hubs, which, in principle, ought to constitute a complete sub-graph (a clique), and the ”spokes”, i.e. the subsets of nodes, each of which is connected in the ultimate structure only with one of the hubs. The paper proposes a relaxation of the hub-and-spoke structure by allowing the hub subgraph not to be complete, but at least connected, with a definite ”degree of completeness” (alpha), from where the name of ”alpha-clique”. It is shown how such structures can be obtained and what are the resulting benefits for various assumptions, regarding such structures. The benefits are measured here with travel times. The desired structures are sought with an evolutionary algorithm. It is shown on an academic example how the results vary and how the conclusions, relevant for practical purposes, can be drawn from such analyses, done with the methods here presented.
Źródło:
Control and Cybernetics; 2018, 47, 2; 173-189
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Identifying main center access hubs in a city using capacity and time criteria. The evolutionary approach
Autorzy:
Owsiński, J. W.
Stańczak, J.
Barski, A.
Sęp, K.
Powiązania:
https://bibliotekanauki.pl/articles/205927.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
transport
urban transport system
Hub and Spoke
Park and Ride
evolutionary algorithm
Opis:
In this paper we consider the well known Hub and Spoke problem, analyzed in the context of Warsaw Public Transport System. Our method was designed for data preprocessing so as to allow using a timetable obtained from the public transport web site after conversion into the required data format. A dedicated evolutionary algorithm method that detects the hubs of almost all available transport means was also developed. The hubs identified are well connected to the center of the city and to other identified hubs (characterized by high capacity or short travel time). These hubs may become the skeleton of the public transport system and, in particular, good points for locating Park and Ride facilities.
Źródło:
Control and Cybernetics; 2016, 45, 2; 207-223
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimization of urban MV multi-loop electric power distribution networks structure using Artificial Intelligence methods
Autorzy:
Parol, M.
Baczyński, D.
Brożek, J.
Powiązania:
https://bibliotekanauki.pl/articles/205678.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
electric power distribution networks
optimization of network structure
evolutionary algorithms
artificial neural networks
Opis:
Urban medium voltage (MV) electric power distribution networks are supplied with primary (HV/MV) substations. These networks supply secondary (MV/LV) transformer substations and are often built as closed structures - loop arrangements. The design problem of optimal urban MV distribution network structure consists of determining the number of primary substations, establishing the number of MV loops supplied with the primary substations, and assigning the secondary MV/LV transformer substations to the MV loops. The optimization task becomes especially complex when the number of the primary substations is greater than one. The minimum of total annual costs is sought. The total annual costs include: fixed (investment) costs, variable (operating) costs and supply-interruption costs. Typical constraints are also accounted for. The so defined optimization problem is a complicated mathematical problem in respect of computational effort. In order to resolve the mathematical model of the optimization problem, evolutionary algorithms and artificial neural networks have been used. Exemplary computational experiments have been executed on the model of urban MV multi-loop electric power distribution networks. The results from the evolutionary algorithm and the artificial neural network calculations have been compared.
Źródło:
Control and Cybernetics; 2012, 41, 3; 667-689
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimal control of multistage deterministic, stochastic and fuzzy processes in the fuzzy environment via an evolutionary algorithm
Autorzy:
Stańczak, J.
Powiązania:
https://bibliotekanauki.pl/articles/970100.pdf
Data publikacji:
2005
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
sterowanie rozmyte
adaptacyjny algorytm ewolucyjny
fuzzy control
multistage optimal fuzzy control
adaptive evolutionary algorithm
Opis:
This paper deals with the problem of control of deterministic, stochastic and fuzzy systems with a fixed termination time and fuzzy constraints imposed on controls and states. Constrains imposed on the system are given as membership functions of particular fuzzy sets. Transition functions for controlled systems are given as a matrix of transitions between states for a deterministic object, a matrix of probabilities of transitions for a stochastic object and a matrix of membership functions of transitions for a fuzzy system. An optimal (or sub-optimal) control is obtained using a specialized evolutionary algorithm (EA), which is a development over the previously used methods based on simple genetic algorithm. The specialized EA seems to be a very effective tool for solving such a class of optimization problems, comparing advantageously with the traditional simple genetic algorithm approach and with the previously used solutions like dynamic programming or branch and bound. The specialization of the applied EA is obtained using dedicated problem encoding, the method of ranking of genetic operators and the controlled selection of population members.
Źródło:
Control and Cybernetics; 2005, 34, 2; 525-552
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid search for optimum in a small implicitly defined region
Autorzy:
Zilinskas, A.
Powiązania:
https://bibliotekanauki.pl/articles/970451.pdf
Data publikacji:
2004
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
wyszukiwanie losowe
optymalizacja ewolucyjna
więzy niejawne
projekt optymalny
random search
evolutionary optimization
implicit constrains
optimal design
Opis:
We consider optimization problems with a small implicitly denned feasible region, and with an objective function corrupted by irregularities, e.g. small noise added to the function values. Known mathematical programming methods with high convergence rate can not, lie applied to such problems. A hybrid technique is developed combining random search for the feasible region of a considered problem, and evolutionary search for the minimum over the found region. The solution results of two test problems and of a difficult real world problem are presented.
Źródło:
Control and Cybernetics; 2004, 33, 4; 599-609
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A rule based machine learning approach to the nonlinear multifingered robot gripper problem
Autorzy:
Abu-Zitar, R.
Al-Fahed Nuseirat, A. M.
Powiązania:
https://bibliotekanauki.pl/articles/970099.pdf
Data publikacji:
2005
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
zacisk robota
programowanie ewolucyjne
komputerowe uczenie się
robot gripper
nonlinear complementarity problem (NCP)
Evolutionary Programming (EP)
machine learning
nearest-classifier-algorithm
Opis:
In this paper, we present a novel method that utilizes the accumulation of knowledge in a rule base for solving the nonlinear frictional gripper problem for both the isotropic and orthotropic cases. The knowledge is discovered and accumulated in a rule base with the aid of a genetic based machine learning mechanism. This machine learning mechanism extracts rules for solving the problem with the help of the Evolutionary Programming [EP) algorithm. The retrievals are done using the nearest-classifier-algorithm. This approach provides online solutions for the problem, and establishes a dynamic and evolving environment that adapts with new and sudden changes on the grip specifications or on the external forces. The resulting grasping forces using the presented method are compared with grasping forces obtained using other methods, such as the Complementarity Problems. The proposed online method could update the needed grasping forces to keep firm grip if the configuration of the forces externally applied to the object is changed. Numerical examples that illustrate the proposed method are presented.
Źródło:
Control and Cybernetics; 2005, 34, 2; 553-573
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-13 z 13

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