%0 Journal Article %1 schmiedle:2002:GPEM %A Schmiedle, Frank %A Drechsler, Nicole %A Grosse, Daniel %A Drechsler, Rolf %D 2002 %J Genetic Programming and Evolvable Machines %K BDD algorithms, genetic heuristic learning, minimization, multi-objective optimization, programming, re-ordering variable %N 4 %P 363--388 %R doi:10.1023/A:1020988925923 %T Heuristic Learning Based on Genetic Programming %V 3 %X In this paper we present an approach to learning heuristics based on Genetic Programming (GP) which can be applied to problems in the VLSI CAD area. GP is used to develop a heuristic that is applied to the problem instance instead of directly solving the problem by application of GP. The GP-based heuristic learning method is applied to one concrete field from the area of VLSI CAD, i.e. minimisation of Binary Decision Diagrams (BDDs). Experimental results are given in order to demonstrate that the GP-based method leads to high quality results that outperform previous methods while the run-times of the resulting heuristics do not increase. Furthermore, we show that by clever adjustment of parameters, further improvements such as the saving of about 50% of the run-time for the learning phase can be achieved.

@article{schmiedle:2002:GPEM, abstract = {In this paper we present an approach to learning heuristics based on Genetic Programming (GP) which can be applied to problems in the VLSI CAD area. GP is used to develop a heuristic that is applied to the problem instance instead of directly solving the problem by application of GP. The GP-based heuristic learning method is applied to one concrete field from the area of VLSI CAD, i.e. minimisation of Binary Decision Diagrams (BDDs). Experimental results are given in order to demonstrate that the GP-based method leads to high quality results that outperform previous methods while the run-times of the resulting heuristics do not increase. Furthermore, we show that by clever adjustment of parameters, further improvements such as the saving of about 50% of the run-time for the learning phase can be achieved.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Schmiedle, Frank and Drechsler, Nicole and Grosse, Daniel and Drechsler, Rolf}, biburl = {https://www.bibsonomy.org/bibtex/27e1317c8b101f8aa8a238b3c4b24c95e/brazovayeye}, doi = {doi:10.1023/A:1020988925923}, interhash = {e43319e428f4ecab83d3ff648e5b893a}, intrahash = {7e1317c8b101f8aa8a238b3c4b24c95e}, issn = {1389-2576}, journal = {Genetic Programming and Evolvable Machines}, keywords = {BDD algorithms, genetic heuristic learning, minimization, multi-objective optimization, programming, re-ordering variable}, month = {December}, notes = {Article ID: 5103874}, number = 4, pages = {363--388}, timestamp = {2008-06-19T17:51:08.000+0200}, title = {Heuristic Learning Based on Genetic Programming}, volume = 3, year = 2002 }