Machine learning often requires the optimization of multiple, partially conflicting objectives. True multi-objective optimization (MOO) methods avoid the need to choose a weighting of the objectives a priori and provide insights about the tradeoffs between the objectives. We extend a state-of-the-art derivative-free Monte Carlo method for MOO, the MO-CMA-ES, to operate on an unbounded set of (n...

With the recent introduction of learning in integrated systems, there is a need to measure the utility of learned knowledge for these more complex systems. A diffiLulty arrises when there are multiple, possibly conflicting, utility metrics to be measured. In this paper, we present schemes which trade off conflicting utility metrics in order to achieve some global performance objectives. In part...

As the size and complexity of medical terminologies increase, terminology modelers are increasingly hampered by lack of tools and methods to manage the development process. This paper presents our use and ongoing evaluation of a description-logic classifier to support cognitive scalability of the underlying terminology and our enhancements to that classifier to support concurrent development ut...

A common approach in multiobjective optimization is to perform the decision making process after the search process: first, a search heuristic approximates the set of Pareto-optimal solutions, and then the decision maker chooses an appropriate trade-off solution from the resulting approximation set. Both processes are strongly affected by the number of optimization criteria. The more objectives...

The optimization is one of the challenging problems in power system. The optimization sometimes is mainly restricted to the minimization of the operating cost. However, the operation of power plants, mostly thermal units, results into various types of emissions like SOx, NOx and COx etc. The environmental concern dictates the minimization of the emissions by the thermal plant. Individually, if ...

In this paper, an active control policy design for a fractional order (FO) financial system is attempted, considering multiple conflicting objectives. An active control template as a nonlinear state feedback mechanism is developed and the controller gains are chosen within a multi-objective optimization (MOO) framework to satisfy the conditions of asymptotic stability, derived analytically. The...

The objectives of concealed data aggregation are to provide end-to-end privacy and en route aggregation of reverse multicast traffic in wireless sensor networks. Privacy homomorphism has been used for realizing these objectives together. Although privacy homomorphism achieves the conflicting objectives, such as privacy and data aggregation, it negatively affects other security objectives such a...