## Search

Now showing items 1-10 of 15

#### Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees

(2023-12)

For a sequence of classification tasks that arrive over time, it is common that tasks
are evolving in the sense that consecutive tasks often have a higher similarity. The
incremental learning of a growing sequence of ...

#### Minimum-Fuel Low-Thrust Trajectory Optimization Via a Direct Adaptive Evolutionary Approach

(2023-11-28)

Space missions with low-thrust propulsion systems are of appreciable interest to space agencies because of their practicality due to higher specific impulses. This research proposes a technique to the solution of minimum-fuel ...

#### Supervised Learning in Time-dependent Environments with Performance Guarantees

(2023-09-25)

In practical scenarios, it is common to learn from a sequence of related problems (tasks).
Such tasks are usually time-dependent in the sense that consecutive tasks are often
significantly more similar. Time-dependency ...

#### Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers

(2023-09)

One particular kind of evolutionary algorithms known as Estimation of Distribution Algorithms (EDAs) has gained the attention of the aerospace industry for its ability to solve nonlinear and complicated problems, particularly ...

#### Adaptive Estimation of Distribution Algorithms for Low-Thrust Trajectory Optimization

(2023-08-02)

A direct adaptive scheme is presented as an alternative approach for minimum-fuel low-thrust trajectory design in non-coplanar orbit transfers, utilizing fitness landscape analysis (FLA). Spacecraft dynamics is modeled ...

#### Efficient Learning of Minimax Risk Classifiers in High Dimensions

(2023-08-01)

High-dimensional data is common in multiple areas, such as health care and genomics, where the
number of features can be tens of thousands. In
such scenarios, the large number of features often leads to inefficient ...

#### Fast K-Medoids With the l_1-Norm

(2023-07-26)

K-medoids clustering is one of the most popular techniques in exploratory data analysis. The most commonly used algorithms to deal with this problem are quadratic on the number of instances, n, and usually the quality of ...

#### New Knowledge about the Elementary Landscape Decomposition for Solving the Quadratic Assignment Problem

(2023-07-15)

Previous works have shown that studying the characteristics of the Quadratic Assignment Problem (QAP) is a crucial step in gaining knowledge that can be used to design tailored meta-heuristic algorithms. One way to analyze ...

#### On the Use of Second Order Neighbors to Escape from Local Optima

(2023-07-12)

Designing efficient local search based algorithms requires to consider the specific properties of the problems. We introduce a simple and effi- cient strategy, the Extended Reach, that escapes from local optima ob- tained ...

#### Minimax Risk Classifiers with 0-1 Loss

(2023-07-01)

Supervised classification techniques use training samples to learn a classification rule with
small expected 0 -1 loss (error probability). Conventional methods enable tractable learning
and provide out-of-sample ...