Evolutionary design of Evolutionary Algorithms

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 e...

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Bibliographic Details
Published in:Genetic programming and evolvable machines 2009-09, Vol.10 (3), p.263-306
Main Authors: Dioşan, Laura, Oltean, Mihai
Format: Article
Language:English
Subjects:
Artificial Intelligence
Biomedical Engineering and Bioengineering
Compilers
Computer Science
Electrical Engineering
Evolution
Evolutionary
Evolutionary algorithms
Genetic algorithms
Genetics
Interpreters
Mathematical models
Optimization
Original Paper
Programming Languages
Programming Techniques
Software Engineering/Programming and Operating Systems
Tasks
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Summary: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.
ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-009-9081-6