Paper presents a computational optimization study using a genetic gender approach for solving multi-objective optimization problems of detection observers. In this methodology the information about an individual gender of all the considered solutions is applied for the purpose of making distinction between different groups of objectives. This information is drawn out of the fitness of individuals and applied during a current parental crossover in the performed evolutionary multi-objective optimization (EMO) processes.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-319-23180-8_23
- Category
- Aktywność konferencyjna
- Type
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language
- angielski
- Publication year
- 2015
