Abstract: A method that imputes missing values while building a predictive model. A population of solutions is created using a data set comprising missing values, wherein each solution comprises parameters of each of the predictive models and the missing values of a data set. Each of the solutions in a population is checked for fitness. After the fitness is checked, the solutions in a population are genetically evolved to establish a successive population of solutions. The process of evolving and checking fitness is continued until a stopping criterion is reached.

Abstract: A system for dynamically allocating resources in a computing grid is provided. The system comprises a resource management component, a service level agreement (SLA) management component and a job scheduling component. The resource management component is configured to determine availability of a set of resources in a computing grid, for executing job requests submitted by users of the computing grid. The SLA management component is configured to manage service level agreements (SLAs) corresponding to the users and perform a negotiation of resources for the users submitting the job requests, in real-time, based on a resource steerability attribute associated with the job requests. The job-scheduling component is configured to schedule the job requests for the users, based on the resource steerability attribute associated with the job requests. The job requests comprise at least one of a job having batch characteristics and a job having interactive characteristics.

Abstract: A technique is provided for developing a propensity model for customer behavior. Multiple biased samples of customer characteristics and results from past activities are established. Initial propensity models are created for each biased sample. The propensity models established for each biased sample are processed separately from the propensity models established for the other biased samples. A genetic algorithm is used to evolve the propensity models. A select number of propensity models that best fit their respective biased samples are compared to a validation sample that is unbiased. A select number of these propensity models that best fit the validation sample are cross-bred into the propensity models established for each biased sample. The propensity models for each biased sample are then processed again using the genetic algorithms. However, a number of elite propensity models are maintained in their original form and not evolved using the genetic algorithm.

Abstract: A technique is provided to coarse-class one or more customer characteristics used in a predictive model. A set of functions are used to represent partition points of the customer characteristic into smaller classes. Each of the final classes of the customer characteristic is represented separately in the predictive model. An initial set of functions may be established to provide an initial set of partitions points of the customer characteristic. The set of functions is then processed using a genetic algorithm to evolve the partition points to new values. Processing the set of partitions using the genetic algorithm may continue until a stopping criterion is reached.

Abstract: A technique is provided to coarse-class one or more customer characteristics used in a predictive model. A set of functions are used to represent partition points of the customer characteristic into smaller classes. Each of the final classes of the customer characteristic is represented separately in the predictive model. An initial set of functions may be established to provide an initial set of partitions points of the customer characteristic. The set of functions is then processed using a genetic algorithm to evolve the partition points to new values. Processing the set of partitions using the genetic algorithm may continue until a stopping criterion is reached.

Abstract: A method that imputes missing values while building a predictive model. A population of solutions is created using a data set comprising missing values, wherein each solution comprises parameters of each of the predictive models and the missing values of a data set. Each of the solutions in a population is checked for fitness. After the fitness is checked, the solutions in a population are genetically evolved to establish a successive population of solutions. The process of evolving and checking fitness is continued until a stopping criterion is reached.

Abstract: A technique is provided for developing a propensity model for customer behavior. Multiple biased samples of customer characteristics and results from past activities are established. Initial propensity models are created for each biased sample. The propensity models established for each biased sample are processed separately from the propensity models established for the other biased samples. A genetic algorithm is used to evolve the propensity models. A select number of propensity models that best fit their respective biased samples are compared to a validation sample that is unbiased. A select number of these propensity models that best fit the validation sample are cross-bred into the propensity models established for each biased sample. The propensity models for each biased sample are then processed again using the genetic algorithms. However, a number of elite propensity models are maintained in their original form and not evolved using the genetic algorithm.