FOGA 2017 - 2017 Foundations of Genetic Algorithms XIV
Date2017-01-12 - 2017-01-15
Deadline2016-08-31
VenueCopenhagen, Denmark
Keywords
Websitehttps://foga-2017.sigevo.org
Topics/Call fo Papers
The 14th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XIV) will take place on January 12-15 in Copenhagen, Denmark.
FOGA is the premier event on the theoretical foundations of evolutionary computation and all kinds of randomised search heuristics, including but not limited to evolutionary algorithms, ant colony optimisation, artificial immune systems and particle swarm optimisation. Accepted papers will be published in post-conference proceedings by ACM Press. The goal of FOGA is to advance the theoretical understanding of evolutionary computation and all kinds of randomised search heuristics, promote theoretical work to the wider community and contribute to making randomised search heuristics more useful in practice. We particularly encourage submissions bridging theory and practice. In addition to strict mathematical investigations, experimental studies contributing towards the theoretical foundations of evolutionary computation methods are also welcome.
Topics include but are not limited to runtime analysis; fitness landscapes and problem difficulty; single- and multi-objective optimisation problems; stochastic and dynamic environments; population dynamics; statistical approaches; self-adaptation; black-box complexity; working principles of all kinds of randomised search heuristics.
FOGA is the premier event on the theoretical foundations of evolutionary computation and all kinds of randomised search heuristics, including but not limited to evolutionary algorithms, ant colony optimisation, artificial immune systems and particle swarm optimisation. Accepted papers will be published in post-conference proceedings by ACM Press. The goal of FOGA is to advance the theoretical understanding of evolutionary computation and all kinds of randomised search heuristics, promote theoretical work to the wider community and contribute to making randomised search heuristics more useful in practice. We particularly encourage submissions bridging theory and practice. In addition to strict mathematical investigations, experimental studies contributing towards the theoretical foundations of evolutionary computation methods are also welcome.
Topics include but are not limited to runtime analysis; fitness landscapes and problem difficulty; single- and multi-objective optimisation problems; stochastic and dynamic environments; population dynamics; statistical approaches; self-adaptation; black-box complexity; working principles of all kinds of randomised search heuristics.
Other CFPs
Last modified: 2016-04-02 20:50:37