Greedy Population Sizing for Evolutionary Algorithms

The number of parameters that need to be man ually tuned to achieve good performance of Evolutionary Algorithms and the dependency of the parameters on each other make this potentially robust and efficient computational method very time consuming and difficult to use. This paper introduces a Greedy Population Sizing method for Evolutionary Algo rithms (GPS-EA), an automated population size tuning method that does not require any population size related parameters to be specified or manually tuned a priori. Theoretical analysis of the number of function evaluations needed by the GPS EA to produce good solutions is provided. We also perform an empirical comparison of the performance of the GPS-EA to the performance of an EA with a manually tuned fixed population size. Both theoretical and empirical results show that using GPS-EA eliminates the need for manually tuning the population size parameter, while finding good solutions. This comes at the price of using twice as many function evaluations as needed by the EA with an optimal fixed population size; this, in practice, is a low price considering the amount of time and effort it takes to find this optimal population size manually

Toward Automating EA Configuration: The Parent Selection Stage

One of the obstacles to Evolutionary Algorithms (EAs) fulfilling their promise as easy to use general-purpose problem solvers, is the difficulty of correctly configuring them for specific problems such as to obtain satisfactory performance. Having a mechanism for automatically configuring parameters and operators of every stage of the evolutionary life-cycle would give EAs a more widely spread popularity in the non-expert community. This paper investigates automatic configuration of one of the stages of the evolutionary life-cycle, the parent selection, via a new concept of semi-autonomous parent selection, where mate selection operators are encoded and evolved as in Genetic Programming. We compare the performance of the EA with semi-autonomous parent selection to that of a manually configured EA on three common test problems to determine the “price” we pay for user-friendliness

Power Grid Protection through Rapid Response Control of FACTS Devices

Recently the use of Flexible AC Transmission System (FACTS) devices has been proposed for enhanced power grid protection and control. Unfortunately, due to their prohibitive costs, it is unlikely that more than a few FACTS devices will be installed on any one power grid. This work studies a particular type of FACTS device: the Unified Power Flow Controller (UPFC). This paper first demonstrates that a few well-placed UPFCs are capable of influencing a large portion of the power grid. Then it introduces partial power flow, a new approach for speeding up cooperative control of UPFCs. Partial power flow relies on recalculating the power flow on only selected sections of the grid. In this work, partial power flow was tested on an optimal power flow (OPF) algorithm for UPFC control, but is not specific to this algorithm. The results presented in this paper show that employing partial power flow leads to a more rapid response to disruptions of the power grid. This paper also outlines further applications of the partial power flow method