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Algoritmos evolutivos para predição de estruturas de proteínas; Evolutionary algorithms, to proteins structures prediction

Lima, Telma Woerle de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 01/09/2006 Português
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A Determinação da Estrutura tridimensional de Proteínas (DEP) a partir da sua seqüência de aminoácidos é importante para a engenharia de proteínas e o desenvolvimento de novos fármacos. Uma alternativa para este problema tem sido a aplicação de técnicas de computação evolutiva. As abordagens utilizando Algoritmos Evolutivos (AEs) tem obtido resultados relevantes, porém estão restritas a pequenas proteínas, com dezenas de aminoácidos e a algumas classes de proteínas. Este trabalho propõe a investigação de uma abordagem utilizando AEs para a predição da estrutura terciária de proteínas independentemente do seu tamanho e classe. Os resultados obtidos demonstram que apesar das dificuldades encontradas a abordagem investigada constitue-se em uma alternativa em relação aos métodos clássicos de determinação da estrutura terciária das proteínas.; Protein structure determination (DEP) from aminoacid sequences is very importante to protein engineering and development of new drugs. Evolutionary computation has been aplied to this problem with relevant results. Nevertheless, Evolutionary Algorithms (EAs) can work with only proteins with few aminoacids and some protein classes. This work proposes an approach using AEs to predict protein tertiary structure independly from their size and class. The obtained results show that...

Análise de similaridades de modelagem no emprego de técnicas conexionistas e evolutivas da inteligência computacional visando à resolução de problemas de otimização combinatorial: estudo de caso - problema do caixeiro viajante.; Similarity analysis for conexionist and evolutionary tecniques of the computational intelligence fild focused on the resolution of combinatorial optimization problems: case study - traveling salesman problem.

Fernandes, David Saraiva Farias
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 08/06/2009 Português
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Este trabalho realiza uma análise dos modelos pertencentes à Computação Neural e à Computação Evolutiva visando identificar semelhanças entre as áreas e sustentar mapeamentos entre as semelhanças identificadas. Neste contexto, a identificação de similaridades visando à resolução de problemas de otimização combinatorial resulta em uma comparação entre a Máquina de Boltzmann e os Algoritmos Evolutivos binários com população composta por um único indivíduo pai e um único indivíduo descendente. Como forma de auxiliar nas análises, o trabalho utiliza o Problema do Caixeiro Viajante como plataforma de ensaios, propondo mapeamentos entre as equações da Máquina de Boltzmann e os operadores evolutivos da Estratégia Evolutiva (1+1)-ES.; An analysis between the Evolutionary Computation and the Neural Computation fields was presented in order to identify similarities and mappings between the theories. In the analysis, the identification of similarities between the models designed for combinatorial optimization problems results in a comparison between the Boltzmann Machine and the Two-Membered Evolutionary Algorithms. In order to analyze the class of combinatorial optimization problems, this work used the Traveling Salesman Problem as a study subject...

Computação evolutiva aplicada a resolução do problema da arvore geradora minima com parametros fuzzy; Evolutionary computation applied to solve the minimum spanning tree problem with fuzzy parameters

Tiago Agostinho de Almeida
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 28/07/2006 Português
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Este trabalho propoe meta-heurýsticas baseadas em tecnicas da computaçao evolutiva, que visam encontrar um conjunto de arvores geradoras mýnimas para problemas de grafos, que possuem incertezas em relaçao as informaçoes associadas aos parametros. Resolver problemas dessa natureza e um processo NP-Completo, pois envolve um numero enorme de comparaçoes. A fim de contornar essa complexidade, este trabalho propoe um algoritmo genetico e um sistema imunologico artificial, capazes de explorar eficientemente o espaco de busca e de obter resultados satisfatorios, sem a necessidade de confrontar todas as solucoes entre si.; This work proposes heuristical approaches based on evolutionary computation, whose goal is to find a set of minimum spanning trees in graphs that contain uncertainties in their parameters. These kind of problems is a NP-hard one, because it involves an enormous number of comparisons. In order to avoid this complexity, this work proposes a genetic algorithm and an artificial immune system, that explore efficiently the search space of solutions to looking for satisfactory results, without the necessity of comparing all possible solutions. Keywords: Fuzzy Graph, Fuzzy Minimum Spanning Tree, Fuzzy Set Theory, Evolutionary Computation...

Evolutionary computation for quality of service internet routing optimization

Rocha, Miguel; Sousa, Pedro; Cortez, Paulo; Rio, Miguel
Fonte: Springer-Verlag Publicador: Springer-Verlag
Tipo: Artigo de Revista Científica
Publicado em /04/2007 Português
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In this work, the main goal is to develop and evaluate a number of optimization algorithms in the task of improving Quality of Service levels in TCP/IP based networks, by configuring the routing weights of link-state protocols such as OSPF. Since this is a complex problem, some meta-heuristics from the Evolutionary Computation arena were considered, working over a mathematical model that allows for flexible cost functions, taking into account several measures of the network behavior such as network congestion and end-to-end delays. A number of experiments were performed, resorting to a large set of network topologies, where Evolutionary Algorithms (EAs), Differential Evolution and some common heuristic methods including local search were compared. EAs make the most promising alternative leading to solutions with an effective network performance even under unfavorable scenarios.

Quality of service constrained routing optimization using evolutionary computation

Rocha, Miguel; Sousa, Pedro; Cortez, Paulo; Rio, Miguel
Fonte: Universidade do Minho Publicador: Universidade do Minho
Tipo: Artigo de Revista Científica
Publicado em //2011 Português
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In this work, a novel optimization framework is proposed that allows the im- provement of Quality of Service levels in TCP/IP based networks, by configuring the routing weights of link-state protocols such as OSPF. Since this is a NP-hard problem, some algorithms from Evolutionary Computation were considered, work- ing over a mathematical model that allows the definition of flexible cost functions that can take into account several measures of the network behaviour, such as net- work congestion and end-to-end delays. A number of experiments were performed, over a large set of network topologies, where Evolutionary Algorithms (EAs), Dif- ferential Evolution, local search methods and common heuristics were compared. EAs make the most promising alternative leading to solutions with an effective net- work performance, even under unfavourable scenarios. A number of state of the art multiobjective optimization algorithms were also tested, but the proposed EAs still hold as the most consistent method for network optimization.; Portuguese National Conference of Rectors (CRUP); British Council Portugal - B-53/05 grant; Nuffield Foundation - NAL/001136/A grant; Engineering and Physical Sciences Research Council - EP/522885 grant; Project SeARCH (Services and Advanced Research Computing with HTC/HPC clusters); Fundação para a Ciência e a Tecnologia (FCT) - Contract CONC-REEQ/443/2001

A software platform for evolutionary computation with pluggable parallelism and quality assurance

Evangelista, Pedro; Gonçalves, Emanuel; Sobral, João Luis; Pinho, Jorge; Maia, Paulo; Rocha, Miguel
Fonte: Springer Publicador: Springer
Tipo: Conferência ou Objeto de Conferência
Publicado em /09/2011 Português
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This paper proposes the Java Evolutionary Computation Library (JECoLi), an adaptable, flexible, extensible and reliable software framework implementing metaheuristic optimization algorithms, using the Java programming language. JECoLi aims to offer a solution suited for the integration of Evolutionary Computation (EC)-based approaches in larger applications, and for the rapid and efficient benchmarking of EC algorithms in specific problems. Its main contributions are (i) the implementation of pluggable parallelization modules, independent from the EC algorithms, allowing the programs to adapt to the available hardware resources in a transparent way, without changing the base code; (ii) a flexible platform for software quality assurance that allows creating tests for the implemented features and for user-defined extensions. The library is freely available as an open-source project.; Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EIA-EIA/115176/2009, Programa COMPETE

Optimization of coefficients of lists of polynomials by evolutionary algorithms

Sendra Pons, Juan Rafael; Winkler, Stephan M.
Fonte: Líceum University Press Publicador: Líceum University Press
Tipo: Artigo de Revista Científica Formato: application/pdf
Português
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We here discuss the optimization of coefficients of lists of polynomials using evolutionary computation. The given polynomials have 5 variables, namely t, a1, a2, a3, a4, and integer coefficients. The goal is to find integer values i, with i 2 {1, 2, 3, 4}, substituting ai such that, after crossing out the gcd (greatest common divisor) of all coefficients of the polynomials, the resulting integers are minimized in absolute value. Evolution strategies, a special class of heuristic, evolutionary algorithms, are here used for solving this problem. In this paper we describe this approach in detail and analyze test results achieved for two benchmark problem instances; we also show a visual analysis of the fitness landscapes of these problem instances; The authors thank Franz Winkler at the Research Institute for Symbolic Computation, Johannes Kepler University Linz, for his advice. R. Sendra is partially supported by the Spanish Ministerio de Economía y Competitividad under the project MTM2011-25816-C02-01 and is a member of the Research Group ASYNACS (Ref. CCEE2011/R34). The authors also thanks members of the Heuristic and Evolutionary Algorithms Laboratory as well as of the Bioinformatics Research Group, University of Applied Sciences Upper Austria...

Introduction to the Applications of Evolutionary Computation in Computer Security and Cryptography

Isasi, Pedro; Hernández, Julio C.
Fonte: Blackwell Publicador: Blackwell
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em /08/2004 Português
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Provides information on the applications of evolutionary computation in computer security and cryptography. Main applications of evolutionary computations in cryptology; Achievements of several researchers in the field of artificial intelligence applications to computer security and cryptology; Examples of successful research.

A study of the effects of clustering and local search on radio network design: evolutionary computation approaches

Sáez, Yago; Zazo, Fernando; Isasi, Pedro
Fonte: IEEE Publicador: IEEE
Tipo: Conferência ou Objeto de Conferência Formato: application/pdf
Publicado em /09/2008 Português
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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 algorithms. Experiments carried out provide empirical evidence of how clustering-based techniques help in improving all algorithms tested. Extensive computational tests, including ones without clustering and 2-OPT, are performed with three evolutionary algorithms: genetic algorithms, memetic algorithms and chromosome appearance probability matrix algorithms.; Eighth International Conference on Hybrid Intelligent Systems. Barcelona, 10-12 September 2008

Application of multiobjective evolutionary techniques for robust portfolio optimization

García-Rodríguez, Sandra
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Tese de Doutorado Formato: text/plain; application/pdf
Português
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The choice of the right way to distribute investor’s wealth among different investment alternatives is one of the basic problems that investors must face. Each of these possible combinations, known as financial portfolios, have some characteristics that make them more or less desirable to the investor depending on his preferences. Therefore, this is the reason why the problem of determining the best proportion of capital to assign to each investment asset, the portfolio optimization, has been one of the core topics in financial management research. Academic literature on this subject is very large and mostly based on the seminal work of H. Markowitz, who suggests the evaluation of portfolios by computing their associated return and risk. Hence, the mentioned problem can be considered as a multiobjective optimization problem where the goal is both maximizing return and minimizing risk of the portfolio at the same time. The opposing nature of these objectives provokes that maximizing one of them increments the other too, and viceversa. Thereby, the solution does not consist of a single asset allocation, but a range of them. These portfolios are the ones with the best risk/return trade-off found and define the Efficient Frontier. Thus...

An evolutionary algorithm for bilevel optimisation of men's team pursuit track cycling

Jordan, C.; Kroeger, T.
Fonte: IEEE; USA Publicador: IEEE; USA
Tipo: Conference paper
Publicado em //2012 Português
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Evolutionary Computation is useful in a broad range of practical applications, however currently generalized algorithms tend to be focused upon solving problems in a theoretical domain. We aim to develop a range of generalised algorithms more suited than current algorithms to practical applications. We contextualize our algorithms using the elite sport of Team Pursuit Track Cycling, which features as part of the Summer Olympics. The sport is fiercely competitive and fractions of a second often separate the world’s leading teams. We set about using Evolutionary Computation to optimise strategies for elite teams of cyclists through changes in the transition timings and the riders power outputs. We trial our range of Evolutionary Computation methods, comparing various algorithms and running them within a time frame suitable for use in a real world environment. We find significantly better results are able to be obtained through our methods than current strategies being developed at an elite level and find the use of the developed algorithms favourable for use in a practical environment.; Claire Diora Jordan and Trent Kroeger

Design of Microwave Absorbing Structure and Microwave Shielding Structure by using Composite Materials, Nanomaterials and Evolutionary Computation

MICHELI, DAVIDE
Fonte: La Sapienza Universidade de Roma Publicador: La Sapienza Universidade de Roma
Tipo: Tese de Doutorado
Português
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Questa tesi raccoglie il lavoro di tre anni di ricerche e studi nel settore dei nanomateriali, nanostrutture ed in generale dei compositi avanzati effettuati presso la Scuola di Ingegneria Aerospaziale della “Sapienza” Università di Roma. In particolare lo scopo è stato quello di approfondire l’interazione tra campi elettromagnetici ed alcune tipologie di compositi avanzati basati essenzialmente su strutture in carbonio e nanomateriali. Questo tentativo ha richiesto un approccio multidisciplinare tra diversi settori scientifici che comprendono quello dei materiali, delle strutture, dei processi di fabbricazione, delle nanotecnologie e dell’elettromagnetismo, i cui concetti di base sono, in questo contesto, dati per acquisiti e per il cui approfondimento si rimanda a testi specifici. L’obiettivo principale è stato quello di utilizzare queste conoscenze trasversali per progettare e costruire nuovi materiali/strutture in grado di assorbire efficacemente i campi elettromagnetici in un ampio intervallo di frequenze ed angoli d’incidenza con molteplici applicazioni anche se l’ambito su cui si è lavorato è quello aerospaziale. Per ottimizzare questi materiali/strutture si è fatto ricorso all’utilizzazione di algoritmi evoluzionistici che sono entrati a pieno titolo nello studio multidisciplinare con uno stretto collegamento tra la teoria sviluppata e le prove di laboratorio atte a validare sperimentalmente i modelli matematici proposti.; This Thesis is focused on scientific research on composite materials electromagnetic characterization and electric conductive polymers applications. Mainly two different composite materials types are taken into account...

Hybrid Evolutionary Computation for Continuous Optimization

Bashir, Hassan A.; Neville, Richard S.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/03/2013 Português
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Hybrid optimization algorithms have gained popularity as it has become apparent there cannot be a universal optimization strategy which is globally more beneficial than any other. Despite their popularity, hybridization frameworks require more detailed categorization regarding: the nature of the problem domain, the constituent algorithms, the coupling schema and the intended area of application. This report proposes a hybrid algorithm for solving small to large-scale continuous global optimization problems. It comprises evolutionary computation (EC) algorithms and a sequential quadratic programming (SQP) algorithm; combined in a collaborative portfolio. The SQP is a gradient based local search method. To optimize the individual contributions of the EC and SQP algorithms for the overall success of the proposed hybrid system, improvements were made in key features of these algorithms. The report proposes enhancements in: i) the evolutionary algorithm, ii) a new convergence detection mechanism was proposed; and iii) in the methods for evaluating the search directions and step sizes for the SQP local search algorithm. The proposed hybrid design aim was to ensure that the two algorithms complement each other by exploring and exploiting the problem search space. Preliminary results justify that an adept hybridization of evolutionary algorithms with a suitable local search method...

Distributed Evolutionary Computation using REST

Castillo, P. A.; Arenas, M. G.; Mora, A. M.; Laredo, J. L. J.; Romero, G.; Rivas, V. M; Merelo, J. J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 25/05/2011 Português
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This paper analises distributed evolutionary computation based on the Representational State Transfer (REST) protocol, which overlays a farming model on evolutionary computation. An approach to evolutionary distributed optimisation of multilayer perceptrons (MLP) using REST and language Perl has been done. In these experiments, a master-slave based evolutionary algorithm (EA) has been implemented, where slave processes evaluate the costly fitness function (training a MLP to solve a classification problem). Obtained results show that the parallel version of the developed programs obtains similar or better results using much less time than the sequential version, obtaining a good speedup.; Comment: Paper 3 for the First International Workshop of Distributed Evolutionary computation in Informal Environments

Evolutionary Computation Algorithms for Cryptanalysis: A Study

Garg, Poonam
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 29/06/2010 Português
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The cryptanalysis of various cipher problems can be formulated as NP-Hard combinatorial problem. Solving such problems requires time and/or memory requirement which increases with the size of the problem. Techniques for solving combinatorial problems fall into two broad groups - exact algorithms and Evolutionary Computation algorithms. An exact algorithms guarantees that the optimal solution to the problem will be found. The exact algorithms like branch and bound, simplex method, brute force etc methodology is very inefficient for solving combinatorial problem because of their prohibitive complexity (time and memory requirement). The Evolutionary Computation algorithms are employed in an attempt to find an adequate solution to the problem. A Evolutionary Computation algorithm - Genetic algorithm, simulated annealing and tabu search were developed to provide a robust and efficient methodology for cryptanalysis. The aim of these techniques to find sufficient "good" solution efficiently with the characteristics of the problem, instead of the global optimum solution, and thus it also provides attractive alternative for the large scale applications. This paper focuses on the methodology of Evolutionary Computation algorithms .; Comment: 5 pages

Prospective Algorithms for Quantum Evolutionary Computation

Sofge, Donald A.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 07/04/2008 Português
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This effort examines the intersection of the emerging field of quantum computing and the more established field of evolutionary computation. The goal is to understand what benefits quantum computing might offer to computational intelligence and how computational intelligence paradigms might be implemented as quantum programs to be run on a future quantum computer. We critically examine proposed algorithms and methods for implementing computational intelligence paradigms, primarily focused on heuristic optimization methods including and related to evolutionary computation, with particular regard for their potential for eventual implementation on quantum computing hardware.; Comment: 8 pages

Browser-based distributed evolutionary computation: performance and scaling behavior

Merelo, J. J.; Mora-Garcia, Antonio; Laredo, J. L. J.; Lupion, Juan; Tricas, Fernando
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 18/01/2007 Português
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The challenge of ad-hoc computing is to find the way of taking advantage of spare cycles in an efficient way that takes into account all capabilities of the devices and interconnections available to them. In this paper we explore distributed evolutionary computation based on the Ruby on Rails framework, which overlays a Model-View-Controller on evolutionary computation. It allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in an evolutionary computation experiment. Using a straightforward farming model, we consider different factors, such as the size of the population used. We are mostly interested in how they impact on performance, but also the scaling behavior when a non-trivial number of computers is applied to the problem. Experiments show the impact of different packet sizes on performance, as well as a quite limited scaling behavior, due to the characteristics of the server. Several solutions for that problem are proposed.; Comment: Submitted to GECCO 2007

Studying Collective Human Decision Making and Creativity with Evolutionary Computation

Sayama, Hiroki; Dionne, Shelley D.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 24/06/2014 Português
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We report a summary of our interdisciplinary research project "Evolutionary Perspective on Collective Decision Making" that was conducted through close collaboration between computational, organizational and social scientists at Binghamton University. We redefined collective human decision making and creativity as evolution of ecologies of ideas, where populations of ideas evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of ideas, each conducted by participating humans. Based on this evolutionary perspective, we generated hypotheses about collective human decision making using agent-based computer simulations. The hypotheses were then tested through several experiments with real human subjects. Throughout this project, we utilized evolutionary computation (EC) in non-traditional ways---(1) as a theoretical framework for reinterpreting the dynamics of idea generation and selection, (2) as a computational simulation model of collective human decision making processes, and (3) as a research tool for collecting high-resolution experimental data of actual collaborative design and decision making from human subjects. We believe our work demonstrates untapped potential of EC for interdisciplinary research involving human and social dynamics.; Comment: 20 pages...

Evolutionary Computation in Astronomy and Astrophysics: A Review

Gutiérrez, José A. García; Cotta, Carlos; Fernández-Leiva, Antonio J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
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In general Evolutionary Computation (EC) includes a number of optimization methods inspired by biological mechanisms of evolution. The methods catalogued in this area use the Darwinian principles of life evolution to produce algorithms that returns high quality solutions to hard-to-solve optimization problems. The main strength of EC is precisely that they provide good solutions even if the computational resources (e.g., running time) are limited. Astronomy and Astrophysics are two fields that often require optimizing problems of high complexity or analyzing a huge amount of data and the so-called complete optimization methods are inherently limited by the size of the problem/data. For instance, reliable analysis of large amounts of data is central to modern astrophysics and astronomical sciences in general. EC techniques perform well where other optimization methods are inherently limited (as complete methods applied to NP-hard problems), and in the last ten years, numerous proposals have come up that apply with greater or lesser success methodologies of evolutional computation to common engineering problems. Some of these problems, such as the estimation of non-lineal parameters, the development of automatic learning techniques, the implementation of control systems...

Distributed Evolutionary Computation: A New Technique for Solving Large Number of Equations

Jahan, Moslema; Hashem, M. M. A.; Shahriar, Gazi Abdullah
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/03/2013 Português
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Evolutionary computation techniques have mostly been used to solve various optimization and learning problems successfully. Evolutionary algorithm is more effective to gain optimal solution(s) to solve complex problems than traditional methods. In case of problems with large set of parameters, evolutionary computation technique incurs a huge computational burden for a single processing unit. Taking this limitation into account, this paper presents a new distributed evolutionary computation technique, which decomposes decision vectors into smaller components and achieves optimal solution in a short time. In this technique, a Jacobi-based Time Variant Adaptive (JBTVA) Hybrid Evolutionary Algorithm is distributed incorporating cluster computation. Moreover, two new selection methods named Best All Selection (BAS) and Twin Selection (TS) are introduced for selecting best fit solution vector. Experimental results show that optimal solution is achieved for different kinds of problems having huge parameters and a considerable speedup is obtained in proposed distributed system.