Contains a collection of source files authored by different people that put together allows the extraction of features from travelling salesman problem instances.

See these papers for a detailed description:

- Discovering the suitability of optimisation algorithms by learning from evolved instances (K. Smith-Miles, J.I. van Hemert), In Annals of Mathematics and Artificial Intelligence, volume 61, 2011.
- Evolving combinatorial problem instances that are difficult to solve (J.I. van Hemert), In Evolutionary Computation, volume 14, 2006.
- Property analysis of symmetric travelling salesman problem instances acquired through evolution (J.I. van Hemert), In Evolutionary Computation in Combinatorial Optimization (G. Raidl, J. Gottlieb, eds.), Springer, 2005.
- Phase transition properties of clustered travelling salesman problem instances generated with evolutionary computation (J.I. van Hemert, N.B. Urquhart), In Parallel Problem Solving from Nature (Xin Yao, Edmund Burke, Jose A. Lozano, Jim Smith, Juan J. Merelo-Guervós, John A. Bullinaria, Jonathan Rowe, Peter Ti\vno Ata Kabán, Hans-Paul Schwefel, eds.), Springer, volume 3242, 2004.

This library implements a framework for an evolutionary algorithm. It aims at including different techniques from the area of evolutionary computation, such as genetic algorithms and genetic programming, in one framework. It is setup such that new projects can be implemented by adjusting only the necessary parts in the library.

Get it from
SourceForge or download below.

*Changelog*

Revisions: problems with latex-3 fixed, allmusic options works again, some new templates, new option template_list.

*Description*

This problem generator for dynamic routing problems can be used to study different aspects of real-time routing by changing parameters of the problem. It is written in Perl and has extensive documentation.

*Downloads*

These are travelling salesman problems that were created by an evolutionary algorithm with the objective function to maximise the time it takes to solve these problems by one of two Lin-Kernighan heuristic solvers.

See these papers for a detailed description:

- Discovering the suitability of optimisation algorithms by learning from evolved instances (K. Smith-Miles, J.I. van Hemert), In Annals of Mathematics and Artificial Intelligence, volume 61, 2011.
- Evolving combinatorial problem instances that are difficult to solve (J.I. van Hemert), In Evolutionary Computation, volume 14, 2006.
- Property analysis of symmetric travelling salesman problem instances acquired through evolution (J.I. van Hemert), In Evolutionary Computation in Combinatorial Optimization (G. Raidl, J. Gottlieb, eds.), Springer, 2005.
- Phase transition properties of clustered travelling salesman problem instances generated with evolutionary computation (J.I. van Hemert, N.B. Urquhart), In Parallel Problem Solving from Nature (Xin Yao, Edmund Burke, Jose A. Lozano, Jim Smith, Juan J. Merelo-Guervós, John A. Bullinaria, Jonathan Rowe, Peter Ti\vno Ata Kabán, Hans-Paul Schwefel, eds.), Springer, volume 3242, 2004.

These are travelling salesman problems that were created by an evolutionary algorithm such that they contain clusters of cities.

See these papers for a detailed description:

- Discovering the suitability of optimisation algorithms by learning from evolved instances (K. Smith-Miles, J.I. van Hemert), In Annals of Mathematics and Artificial Intelligence, volume 61, 2011.
- Evolving combinatorial problem instances that are difficult to solve (J.I. van Hemert), In Evolutionary Computation, volume 14, 2006.
- Property analysis of symmetric travelling salesman problem instances acquired through evolution (J.I. van Hemert), In Evolutionary Computation in Combinatorial Optimization (G. Raidl, J. Gottlieb, eds.), Springer, 2005.
- Phase transition properties of clustered travelling salesman problem instances generated with evolutionary computation (J.I. van Hemert, N.B. Urquhart), In Parallel Problem Solving from Nature (Xin Yao, Edmund Burke, Jose A. Lozano, Jim Smith, Juan J. Merelo-Guervós, John A. Bullinaria, Jonathan Rowe, Peter Ti\vno Ata Kabán, Hans-Paul Schwefel, eds.), Springer, volume 3242, 2004.

These are binary constraint satisfaction problems created to test the performance of algorithms. The first set covers the landscape of solvable to non-solvable problems and covers the phase transition of easy to hard to easy. The second set comprises small to large problems.

See these papers for a detailed description:

- Comparing Evolutionary Algorithms on Binary Constraint Satisfaction Problems (B.G.W. Craenen, A.E. Eiben, J.I. van Hemert), In IEEE Transactions on Evolutionary Computation, volume 7, 2003.
- Application of Evolutionary Computation to Constraint Satisfaction and Data Mining (J.I. van Hemert), PhD thesis, Leiden University, 2002. (ISBN: 90-6734-057-X)