## Bioinspired computation in combinatorial optimization: algorithms and their computational complexity

Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

### Standard

**Bioinspired computation in combinatorial optimization: algorithms and their computational complexity.** / Neumann, Frank; Witt, Carsten.

Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

### Harvard

*Proceedings of the fourteenth international conference on Genetic and evolutionary computation: Companion.*Association for Computing Machinery, pp. 1035-1058. DOI: 10.1145/2330784.2330928

### APA

*Proceedings of the fourteenth international conference on Genetic and evolutionary computation: Companion.*(pp. 1035-1058). Association for Computing Machinery. DOI: 10.1145/2330784.2330928

### CBE

### MLA

*Proceedings of the fourteenth international conference on Genetic and evolutionary computation: Companion.*Association for Computing Machinery. 2012. 1035-1058. Available: 10.1145/2330784.2330928

### Vancouver

### Author

### Bibtex

}

### RIS

TY - GEN

T1 - Bioinspired computation in combinatorial optimization: algorithms and their computational complexity

AU - Neumann,Frank

AU - Witt,Carsten

PY - 2012

Y1 - 2012

N2 - Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these algorithms. This tutorials explains the most important results achieved in this area.<br/><br/>The presenters show how runtime behavior can be analyzed in a rigorous way, in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems.<br/><br/>The tutorial is based on a book written by the authors with the same title. Further information about the book can be found at www.bioinspiredcomputation.com.

AB - Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these algorithms. This tutorials explains the most important results achieved in this area.<br/><br/>The presenters show how runtime behavior can be analyzed in a rigorous way, in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems.<br/><br/>The tutorial is based on a book written by the authors with the same title. Further information about the book can be found at www.bioinspiredcomputation.com.

U2 - 10.1145/2330784.2330928

DO - 10.1145/2330784.2330928

M3 - Article in proceedings

SN - 978-1-4503-1178-6

SP - 1035

EP - 1058

BT - Proceedings of the fourteenth international conference on Genetic and evolutionary computation

PB - Association for Computing Machinery

ER -