%0 Conference Paper %1 fukunaga:1995:dsef %A Fukunaga, Alex S. %A Kahng, Andrew B. %B 1995 IEEE Conference on Evolutionary Computation %C Perth, Australia %D 1995 %I IEEE Press %K algorithms, genetic programming %P 182--187 %T Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evolution Function %U http://citeseer.ist.psu.edu/fukunaga95improving.html %V 1 %X Traditional evolutionary optimization algorithms assume a static environment in which solutions are evolved. Incremental evolution is an approach through which a dynamic evaluation function is scaled over time in order to improve the performance of evolutionary optimization. In this paper, we present empirical results that demonstrate the effectiveness of this approach for genetic programming. Using two domains, a two-agent pursuit-evasion game and the Tracker trail-following task, we demonstrate that incremental evolution is most successful when applied near the beginning of an evolutionary run. We also show that incremental evolution can be successful when the intermediate evaluation functions are more difficult than the target evaluation function, as well as they are easier than the target function.

@inproceedings{fukunaga:1995:dsef, abstract = {Traditional evolutionary optimization algorithms assume a static environment in which solutions are evolved. Incremental evolution is an approach through which a dynamic evaluation function is scaled over time in order to improve the performance of evolutionary optimization. In this paper, we present empirical results that demonstrate the effectiveness of this approach for genetic programming. Using two domains, a two-agent pursuit-evasion game and the Tracker trail-following task, we demonstrate that incremental evolution is most successful when applied near the beginning of an evolutionary run. We also show that incremental evolution can be successful when the intermediate evaluation functions are more difficult than the target evaluation function, as well as they are easier than the target function.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Perth, Australia}, author = {Fukunaga, Alex S. and Kahng, Andrew B.}, biburl = {https://www.bibsonomy.org/bibtex/288851e07457f6b6d781469e287063a15/brazovayeye}, booktitle = {1995 IEEE Conference on Evolutionary Computation}, broken = {http://www-aig.jpl.nasa.gov/home/fukunaga/publications/ICEC95-priming-camera.ps}, interhash = {3429dc3f0615141dc3eaee8fbb329d38}, intrahash = {88851e07457f6b6d781469e287063a15}, keywords = {algorithms, genetic programming}, month = {29 November - 1 December}, notes = {ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva.}, pages = {182--187}, publisher = {IEEE Press}, publisher_address = {Piscataway, NJ, USA}, size = {6 pages}, timestamp = {2008-06-19T17:39:54.000+0200}, title = {Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evolution Function}, url = {http://citeseer.ist.psu.edu/fukunaga95improving.html}, volume = 1, year = 1995 }