Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A Game-Theoretic Approach to the Simple Coevolutionary Algorithm
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
New methods for competitive coevolution
Evolutionary Computation
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
Evolutionary optimization of multi-agent controlstrategies for electric vehicle charging
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Evolutionary algorithm based control policies for flexible optimal power flow over time
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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The optimal power flow (OPF) is one of the central optimization problems in power grid engineering, building an essential tool for numerous control as well as planning issues. Methods for solving the OPF that mainly treat steady-state situations have been studied extensively, ignoring uncertainties of system variables as well as their volatile behavior. While both the economical as well as well as technical importance of accurate control is high, especially for power flow control in dynamic and uncertain power systems, methods are needed that provide (near-) optimal actions quickly, eliminating issues on convergence speed or robustness of the optimization. This paper shows an approximate policy-based control approach where optimal actions are derived from policies that are learned offline, but that later provide quick and accurate control actions in volatile situations. These policies are evolved using genetic programming, where multiple and interdependent policies are learned synchronously with simulation-based optimization. Finally, an approach is available for learning fast and robust power flow control policies suitable to highly dynamic power systems such as smart electric grids.