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  1. Genetic Programming and Evolvable Machines
  2. Genetic Programming and Evolvable Machines : Volume 10
  3. Genetic Programming and Evolvable Machines : Volume 10, Issue 3, September 2009
  4. Evolutionary design of Evolutionary Algorithms
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Genetic Programming and Evolvable Machines : Volume 18
Genetic Programming and Evolvable Machines : Volume 17
Genetic Programming and Evolvable Machines : Volume 16
Genetic Programming and Evolvable Machines : Volume 15
Genetic Programming and Evolvable Machines : Volume 14
Genetic Programming and Evolvable Machines : Volume 13
Genetic Programming and Evolvable Machines : Volume 12
Genetic Programming and Evolvable Machines : Volume 11
Genetic Programming and Evolvable Machines : Volume 10
Genetic Programming and Evolvable Machines : Volume 10, Issue 4, December 2009
Genetic Programming and Evolvable Machines : Volume 10, Issue 3, September 2009
A three-step decomposition method for the evolutionary design of sequential logic circuits
Evolutionary design of Evolutionary Algorithms
Semantic analysis of program initialisation in genetic programming
Genetic Programming and Evolvable Machines : Volume 10, Issue 2, June 2009
Genetic Programming and Evolvable Machines : Volume 10, Issue 1, March 2009
Genetic Programming and Evolvable Machines : Volume 9
Genetic Programming and Evolvable Machines : Volume 8
Genetic Programming and Evolvable Machines : Volume 7
Genetic Programming and Evolvable Machines : Volume 6
Genetic Programming and Evolvable Machines : Volume 5
Genetic Programming and Evolvable Machines : Volume 4
Genetic Programming and Evolvable Machines : Volume 3
Genetic Programming and Evolvable Machines : Volume 2
Genetic Programming and Evolvable Machines : Volume 1

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Evolutionary design of Evolutionary Algorithms

Content Provider SpringerLink
Author Dioşan, Laura Oltean, Mihai
Copyright Year 2009
Abstract Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.
Starting Page 263
Ending Page 306
Page Count 44
File Format PDF
ISSN 13892576
Journal Genetic Programming and Evolvable Machines
Volume Number 10
Issue Number 3
e-ISSN 15737632
Language English
Publisher Springer US
Publisher Date 2009-03-20
Publisher Place Boston
Access Restriction Subscribed
Subject Keyword Genetic algorithms Evolving evolutionary algorithms Meta genetic programming Function optimization Biomedical Engineering Programming Languages, Compilers, Interpreters Programming Techniques Software Engineering/Programming and Operating Systems Artificial Intelligence (incl. Robotics) Electrical Engineering
Content Type Text
Resource Type Article
Subject Theoretical Computer Science Hardware and Architecture Computer Science Applications Software
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