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Genetic Algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem's…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-01-23 Matheus F. Torquato , Marcelo A. C. Fernandes

We present a new method for stochastic shape optimisation of engineering structures. The method generalises an existing deterministic scheme, in which the structure is represented and evolved by a level-set method coupled with mathematical…

Statistical Mechanics · Physics 2017-09-13 Lester O. Hedges , H. Alicia Kim , Robert L. Jack

Flexible job shop scheduling has been noticed as an effective manufacturing system to cope with rapid development in today's competitive environment. Flexible job shop scheduling problem (FJSSP) is known as a NP-hard problem in the field of…

Optimization and Control · Mathematics 2012-07-11 Sayedmohammadreza Vaghefinezhad , Kuan Yew Wong

This article describes a model and an exact solution method for facility location problems with decision-dependent uncertainties. The model allows characterizing the probability distribution of the random elements as a function of the…

Optimization and Control · Mathematics 2025-09-15 Giovanni Pantuso

In this paper, we propose a stochastic search algorithm for solving general optimization problems with little structure. The algorithm iteratively finds high quality solutions by randomly sampling candidate solutions from a parameterized…

Optimization and Control · Mathematics 2013-01-08 Enlu Zhou , Jiaqiao Hu

The computational efficiency of stochastic simulation algorithms is notoriously limited by the kinetic trapping of the simulated trajectories within low energy basins. Here we present a new method that overcomes kinetic trapping while still…

Statistical Mechanics · Physics 2014-12-08 Manuel Athènes , Vasily V. Bulatov

We propose a framework of genetic algorithms which use multi-level hierarchies to solve an optimization problem by searching over the space of simpler objective functions. We solve a variant of Travelling Salesman Problem called…

Neural and Evolutionary Computing · Computer Science 2019-08-06 Harshavardhan Kamarthi , Kousik Krishnan

We introduce an evolutionary stochastic-local-search (SLS) algorithm for addressing a generalized version of the so-called 1/V/D/R cutting-stock problem. Cutting-stock problems are encountered often in industrial environments and the…

Neural and Evolutionary Computing · Computer Science 2017-07-28 Georgios C. Chasparis , Michael Rossbory , Verena Haunschmid

This paper presented a genetic algorithm (GA) to solve the container storage problem in the port. This problem is studied with different container types such as regular, open side, open top, tank, empty and refrigerated containers. The…

Neural and Evolutionary Computing · Computer Science 2013-03-06 I. Ayachi , R. Kammarti , M. Ksouri , P. Borne , : , LAGIS , Ecole Centrale de Lille , : , LACS , Ecole Nationale des Ingenieurs de Tunis

Our study focuses on fractional order compartment models derived from underlying physical stochastic processes, providing a more physically grounded approach compared to models that use the dynamical system approach by simply replacing…

Differentiable simulation is a promising toolkit for fast gradient-based policy optimization and system identification. However, existing approaches to differentiable simulation have largely tackled scenarios where obtaining smooth…

Machine Learning · Statistics 2022-07-04 Rika Antonova , Jingyun Yang , Krishna Murthy Jatavallabhula , Jeannette Bohg

We present an algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte…

Computational Physics · Physics 2016-08-24 Daniel A. Charlebois , Jukka Intosalmi , Dawn Fraser , Mads Kaern

In many global Optimization Problems, it is required to evaluate a global point (min or max) in large space that calculation effort is very high. In this paper is presented new approach for optimization problem with subdivision labeling…

Neural and Evolutionary Computing · Computer Science 2013-07-23 Masoumeh Vali

Planning for sequential robotics tasks often requires integrated symbolic and geometric reasoning. TAMP algorithms typically solve these problems by performing a tree search over high-level task sequences while checking for kinematic and…

The problem of firebreak placement is crucial for fire prevention, and its effectiveness at landscape scale will depend on their ability to impede the progress of future wildfires. To provide an adequate response, it is therefore necessary…

Artificial Intelligence · Computer Science 2023-11-30 David Palacios-Meneses , Jaime Carrasco , Sebastián Dávila , Maximiliano Martínez , Rodrigo Mahaluf , Andrés Weintraub

Topology Optimization seeks to find the best design that satisfies a set of constraints while maximizing system performance. Traditional iterative optimization methods like SIMP can be computationally expensive and get stuck in local…

Machine Learning · Computer Science 2023-03-20 Giorgio Giannone , Faez Ahmed

This paper presents a novel approach to solve capacitated facility location problems (FLP) that encompass various resource allocation problems. FLPs are a class of NP-hard combinatorial optimization problems, involving optimal placement and…

Optimization and Control · Mathematics 2025-04-03 Alisina Bayati , Dhananjay Tiwari , Srinivasa Salapaka

Single Row Equidistant Facility Layout Problem SREFLP is with an NP-Hard nature to mimic material handling costs along with equally spaced straight-line facilities layout. Based on literature, it is obvious that efforts of researchers for…

Data Structures and Algorithms · Computer Science 2022-12-22 Shrouq Gamal , Ahmed A. Hawam , Ahmed M. El-Kassas

This paper addresses the optimization of scheduling for workers at a logistics depot using a combination of genetic algorithm and simulated annealing algorithm. The efficient scheduling of permanent and temporary workers is crucial for…

Neural and Evolutionary Computing · Computer Science 2024-05-21 Jinxin Xu , Haixin Wu , Yu Cheng , Liyang Wang , Xin Yang , Xintong Fu , Yuelong Su

We propose a new approach to solve optimal stopping problems via simulation. Working within the backward dynamic programming/Snell envelope framework, we augment the methodology of Longstaff-Schwartz that focuses on approximating the…

Computational Finance · Quantitative Finance 2015-09-04 Robert B. Gramacy , Mike Ludkovski