By Topic

Evolutionary Computation, IEEE Transactions on

Issue 4 • Date Aug. 2003

Filter Results

Displaying Results 1 - 6 of 6
  • Elitism-based compact genetic algorithms

    Publication Year: 2003 , Page(s): 367 - 385
    Cited by:  Papers (54)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1156 KB) |  | HTML iconHTML  

    This paper describes two elitism-based compact genetic algorithms (cGAs)-persistent elitist compact genetic algorithm (pe-cGA), and nonpersistent elitist compact genetic algorithm (ne-cGA). The aim is to design efficient cGAs by treating them as estimation of distribution algorithms (EDAs) for solving difficult optimization problems without compromising on memory and computation costs. The idea is... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Society and civilization: An optimization algorithm based on the simulation of social behavior

    Publication Year: 2003 , Page(s): 386 - 396
    Cited by:  Papers (73)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (813 KB) |  | HTML iconHTML  

    The ability to mutually interact is a fundamental social behavior in all human and insect societies. Social interactions enable individuals to adapt and improve faster than biological evolution based on genetic inheritance alone. This is the driving concept behind the optimization algorithm introduced in this paper that makes use of the intra and intersociety interactions within a formal society a... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Fuzzy coding of genetic algorithms

    Publication Year: 2003 , Page(s): 344 - 355
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1546 KB) |  | HTML iconHTML  

    A new chromosome encoding method, named fuzzy coding, is proposed for representing real number parameters in a genetic algorithm. Fuzzy coding provides the value of a parameter on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree of membership. Thus, it represents the knowledge associated with each parameter and is an indirect method of encoding comp... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Rank-density-based multiobjective genetic algorithm and benchmark test function study

    Publication Year: 2003 , Page(s): 325 - 343
    Cited by:  Papers (56)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1109 KB) |  | HTML iconHTML  

    Concerns the use of evolutionary algorithms (EA) in solving multiobjective optimization problems (MOP). We propose the use of a rank-density-based genetic algorithm (RDGA) that synergistically integrates selected features from existing algorithms in a unique way. A new ranking method, automatic accumulated ranking strategy, and a "forbidden region" concept are introduced, completed by a revised ad... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Directed variation in evolution strategies

    Publication Year: 2003 , Page(s): 356 - 366
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (632 KB) |  | HTML iconHTML  

    Biological evolution gives rise to self-organizing phenomena. Inspired by this theory, directed variation is added to the (μ, λ) evolution strategies (ES) algorithm and it is called directed variation ES (DVES). In DVES, some neighboring individuals in the population mutate correlatively according to the distribution of the whole population. Experimental results showed that, with the sam... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • SOGARG: A self-organized genetic algorithm-based rule generation scheme for fuzzy controllers

    Publication Year: 2003 , Page(s): 397 - 415
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1240 KB) |  | HTML iconHTML  

    This paper presents a self-organized genetic algorithm-based rule generation (SOGARG) method for fuzzy logic controllers. It is a three-stage hierarchical scheme that does not require any expert knowledge and input-output data. The first stage selects rules required to control the system in the vicinity of the set point. The second stage extends this to the entire input space, giving a rulebase th... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

IEEE Transactions on Evolutionary Computation publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.
 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief

Dr. Kay Chen Tan (IEEE Fellow)

Department of Electrical and Computer Engineering

National University of Singapore

Singapore 117583

Email: eletankc@nus.edu.sg

Website: http://vlab.ee.nus.edu.sg/~kctan