The geometric programming problem is an important optimization technique that often used to solve different nonlinear problems and engineering problems. models are commonly generally based on deterministic accurate parameters. However, it observed in real-world problems, the parameters frequently inaccurate ambiguous. In this paper, we consider chance-constrained with uncertain coefficients tec...

Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently than operating syntax like most GP systems. Efficient implementations in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, f...

This paper proposes formulating conceptual-stage vessel design optimization problem as a geometric program, which can be transformed into convex problem. The programming modeling approach offers significant advantages in efficiency, reliability and automation over the general nonlinear methods typically used naval architecture. Focusing on battery-electric vessels, compatible expressions are de...

In this paper, we present a new geometric approach for sensitivity analysis in linear programming that is computationally practical decision-maker to study the behavior of optimal solution problem under changes program data. First, fix feasible domain (fix constraints). Then, geometrically formulate problem. Next, give equivalent formulation using notions affine geometry which consists write co...