Abstract
This paper exemplarily points out how essential genetic information evolves during the runs of selected GA-variants. The algorithmic enhancements to a standard genetic algorithm certify the survival of essential genetic information by supporting the survival of relevant alleles rather than the survival of above average chromosomes. This is achieved by defining the survival probability of a new child chromosome depending on the child's fitness in comparison to the fitness values of its own parents. The main aim of this paper is to explain important properties of the discussed algorithm variants in a rather intuitive way. Aspects for meaningful and practically more relevant generalizations as well as more sophisticated experimental analyses are indicated.
| Original language | English |
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| Title of host publication | 2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09 |
| Publisher | ACM Sigevo |
| Pages | 787-790 |
| Number of pages | 4 |
| ISBN (Print) | 9781605583266 |
| DOIs | |
| Publication status | Published - 2009 |
| Event | 2009 World Summit on Genetic and Evolutionary Computation (2009 GEC Summit) - Shanghai, China Duration: 12 Jun 2009 → 14 Jun 2009 http://www.sigevo.org/gec-summit-2009/ |
Publication series
| Name | 2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09 |
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Conference
| Conference | 2009 World Summit on Genetic and Evolutionary Computation (2009 GEC Summit) |
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| Country/Territory | China |
| City | Shanghai |
| Period | 12.06.2009 → 14.06.2009 |
| Internet address |
Keywords
- Genetic algorithms
- Population diversity analysis
- Premature convergence
- Selection
- Self-adaptivity