Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
|Title||IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence)|
|Place of publication||New York|
|Publisher||IEEE Computer Society|
|Number of pages||6|
|Conference||IEEE Congress on Evolutionary Computation|
|Period||1/06/08 → 6/06/08|
This paper describes two directed intervention crossover approaches that are applied to the problem of deriving optimal cancer chemotherapy treatment schedules. Unlike traditional uniform crossover (UC), both the calculated expanding bin (CalEB) method and targeted intervention with stochastic selection (TInSSel) approaches actively choose an intervention level and spread based on the fitness of the parents selected for crossover. Our results indicate that these approaches lead to significant improvements over UC when applied to cancer chemotherapy scheduling.