NDLI: Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA '17)

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Fast and Powerful Hashing using Tabulation

An Application of Stochastic Differential Equations to Evolutionary Algorithms

Runtime Analysis of a Discrete Particle Swarm Optimization Algorithm on Sorting and OneMax

Resampling vs Recombination: a Statistical Run Time Estimation

On the Use of the Dual Formulation for Minimum Weighted Vertex Cover in Evolutionary Algorithms

Analysis of the (1+1) EA on Subclasses of Linear Functions under Uniform and Linear Constraints

Analysis of the Clearing Diversity-Preserving Mechanism

Lower Bounds on the Run Time of the Univariate Marginal Distribution Algorithm on OneMax

Convergence of Factored Evolutionary Algorithms

Hypervolume Subset Selection for Triangular and Inverted Triangular Pareto Fronts of Three-Objective Problems

Quality Gain Analysis of the Weighted Recombination Evolution Strategy on General Convex Quadratic Functions

On the Statistical Learning Ability of Evolution Strategies

Qualitative and Quantitative Assessment of Step Size Adaptation Rules

Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling

Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA '17)

Content Provider	ACM Digital Library
Editor	Witt, Carsten Sudholt, Dirk Igel, Christian
Copyright Year	2017
Abstract	It is our great pleasure to welcome you to the proceedings of the 14th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XIV). Since the first FOGA in 1990, the workshop has established itself as the premier event on the foundations of evolutionary computation. The goal of FOGA is to advance the theoretical understanding of randomised search heuristics and to contribute to making these algorithms more useful in practice. The workshop invites submissions on all kinds of randomised search heuristics, including but not limited to evolutionary algorithms, ant colony optimisation, artificial immune systems, particle swarm optimisation, simulated annealing, Bayesian optimisation, and other Monte Carlo methods for search and optimisation. Contributions bridging theory and practice are particularly encouraged. In addition to rigorous mathematical investigations, experimental studies contributing towards the theoretical foundations of randomised search heuristics are also welcome at FOGA. FOGA 2017 in Copenhagen, Denmark, was the first to be hosted in a Scandinavian country. We had 23 submissions, out of which 13 papers were accepted for inclusion in these post-conference proceedings. All submissions were thoroughly peer-reviewed, including a second review phase for conditionally accepted manuscripts. We had 23 registrations from 8 different countries from four continents. The presented papers covered an impressive variety of topics: discrete and continuous optimisation problems, single- and multi-objective optimisation, and various search heuristics, including evolution strategies, evolutionary algorithms, factored evolutionary algorithms, particle swarm optimisation, and estimation-of-distribution algorithms. We thank Mikkel Thorup giving an inspiring keynote on Fast and Powerful Hashing using Tabulation and the University of Copenhagen for providing a spectacular setting in Festauditoriet, the picturesque lecture hall in the former Royal Veterinary and Agricultural University.
ISBN	9781450346511
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-01-12
Access Restriction	Subscribed
Content Type	Text
Resource Type	Conference Proceedings

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