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Found 12 papers, 0 papers with code
Evolutionary Optimization for Decision Making under Uncertainty
Optimizing decision problems under uncertainty can be done using a variety of solution methods.
Evolving Accuracy: A Genetic Algorithm to Improve Election Night Forecasts
In this paper, we apply genetic algorithms to the field of electoral studies.
Revolutionary Algorithms
These algorithms seek to extend the evolutionary and cultural aspects of the former to approaches with a notion of the political.
An Evolutionary Approach towards Clustering Airborne Laser Scanning Data
While the original purpose of LiDAR systems was to determine the altitude of ground elevations, newer full wave systems provide additional information that can be used on classifying the type of ground cover and the generation of maps.
A Genetic Algorithm to Optimize a Tweet for Retweetability
A genetic algorithm is used to optimize message composition, so that the reach of a message is increased.
Automated Classification of Airborne Laser Scanning Point Clouds
Making sense of the physical world has always been at the core of mapping.
Modeling multi-stage decision optimization problems
Multi-stage optimization under uncertainty techniques can be used to solve long-term management problems.
Effects of Sampling Methods on Prediction Quality. The Case of Classifying Land Cover Using Decision Trees
Clever sampling methods can be used to improve the handling of big data and increase its usefulness.
The Role of Emotions in Propagating Brands in Social Networks
While the function of emotions in offline marketing in general and word of mouth marketing in particular is rather well understood, online marketing can only offer a limited view on the function of emotions.
An Evolutionary Optimization Approach to Risk Parity Portfolio Selection
In this paper we present an evolutionary optimization approach to solve the risk parity portfolio selection problem.
Computing trading strategies based on financial sentiment data using evolutionary optimization
In this paper we apply evolutionary optimization techniques to compute optimal rule-based trading strategies based on financial sentiment data.
Efficient and robust calibration of the Heston option pricing model for American options using an improved Cuckoo Search Algorithm
In this paper an improved Cuckoo Search Algorithm is developed to allow for an efficient and robust calibration of the Heston option pricing model for American options.
