paper-with-me

홈 › Papers

Hidden-Variables Genetic Algorithm for Variable-Size Design Space Optimal Layout Problems with Application to Aerospace Vehicles

2022-12-21 · Juliette Gamot, Mathieu Balesdent, Arnault Tremolet, Romain Wuilbercq, Nouredine Melab, El-Ghazali Talbi

The optimal layout of a complex system such as aerospace vehicles consists in placing a given number of components in a container in order to minimize one or several objectives under some geometrical or functional constraints. This paper presents an extended formulation of this problem as a variable-size design space (VSDS) problem to take into account a large number of architectural choices and components allocation during the design process. As a representative example of such systems, considering the layout of a satellite module, the VSDS aspect translates the fact that the optimizer has to choose between several subdivisions of the components. For instance, one large tank of fuel might be placed as well as two smaller tanks or three even smaller tanks for the same amount of fuel. In order to tackle this NP-hard problem, a genetic algorithm enhanced by an adapted hidden-variables mechanism is proposed. This latter is illustrated on a toy case and an aerospace application case representative to real world complexity to illustrate the performance of the proposed algorithms. The results obtained using the proposed mechanism are reported and analyzed.

📄 PDF Abstract BibTeX arXiv:2212.11011

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Genetic heterogeneity analysis using genetic algorithm and network science

2023-08-12 · Zhendong Sha, Yuanzhu Chen, Ting Hu

Through genome-wide association studies (GWAS), disease susceptible genetic variables can be identified by comparing the genetic data of individuals with and without a specific disease. However, the discovery of these as…

feature selection

IHCV: Discovery of Hidden Time-Dependent Control Variables in Non-Linear Dynamical Systems

2023-04-05 · Juan Munoz, Subash Balsamy, Juan P. Bernal-Tamayo, Ali Balubaid 외

Discovering non-linear dynamical models from data is at the core of science. Recent progress hinges upon sparse regression of observables using extensive libraries of candidate functions. However, it remains challenging …

Benchmarking

Exact and approximate inference in graphical models: variable elimination and beyond

2015-06-29 · Nathalie Peyrard, Marie-Josée Cros, Simon de Givry, Alain Franc 외

Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks i…

Combinatorial Causal Bandits

2022-06-04 · Shi Feng, Wei Chen

In combinatorial causal bandits (CCB), the learning agent chooses at most $K$ variables in each round to intervene, collects feedback from the observed variables, with the goal of minimizing expected regret on the target…

Causal Inference

Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies

2024-09-22 · Hyunchai Jeong, Adiba Ejaz, Jin Tian, Elias Bareinboim

Testing a hypothesized causal model against observational data is a key prerequisite for many causal inference tasks. A natural approach is to test whether the conditional independence relations (CIs) assumed in the mode…

Causal Inference