Interactive dynamic production by genetic algorithms
Marco Baioletti, Alfredo Milani, Valentina Poggioni, Silvia Suriani · 2007
In this work we introduce an adaptive genetic algorithm for solving a class of interactive production problems in a dy-namical environment. In the interactive production prob-lem, a system continuously generates product instances which should meet the requirements of a market of cus-tomers/agents which are unknown to it. The only way for the system to know the evaluation of a product instance is the feedback obtained after delivering it to the customer. In a dynamical environment the domain of the products is changing and the customer/agents are changing their pref-erences over the time. This scenario is common to many IT services and products which are continuously delivered to a mass of anonymous users. The proposed algorithm employs typical genetic operators in order to optimize the product delivered and to adapt it to the environment feed-back and evolution. Differently from classical GA the goal of such system is to maximize the average result instead of determining the best optimal solution. Experimental re-sults are promising and show interesting properties of the adaptive behavior of GA techniques.