...Bikash Kanti Sarkar; Ambuj Kumar; Shib Sankar Sana Purpose This study aims to propose a genetic algorithm ( GA )-based feature reduction ( FR ) approach applicable to any classification data set, regardless of domain, dimensionality and size of the data set, with any number of classes and data...

...Jose M.J.; Abdul Nazar K.P. Purpose This study aims to develop a novel hybrid Genetic Algorithm ( GA ) for Flexible Job-shop Scheduling Problems ( FJSP ) to improve makespan. Design/methodology/approach DIGA, a Reinforcement Learning ( RL )-driven algorithmic framework is proposed...

... for a given power target. Third, three metaheuristic solvers – the Genetic Algorithm ( GA ), Particle Swarm Optimization ( PSO ) and Differential Evolution ( DE ) – are benchmarked for speed, accuracy and robustness. Optuna-based hyperparameter tuning was conducted for both the surrogate model and the GA...

... (as defined by fitness of best solution) of genetic algorithms (solid lines; green, blue, red) and baseline algorithms (greedy – dotted grey, greedy and hill-climbing – dashed black) Note(s): The average is found by performing 20 runs of each algorithm set-up and the shaded regions represent the 95...

...Mariusz Kmiecik Purpose The purpose of this paper is to present the design and implementation of a genetic algorithm (GA), using a large language model (LLM) for optimizing the delivery scheduling process in warehouses of third-party logistics (3PL) companies, within the context of a simplified...

... envelopment analysis Two-stage system Intermediate weights Genetic algorithm GA-FNDEA algorithm Evaluating the efficiency and performance of decision-making units (DMUs) is a noticeable issue for managers in the real world. An appropriate method to measure the relative efficiency of peer DMUs...

... the relative locations of the machines and cells both in left-right and below-up direction. This approach results in a linear model as the binary variables are eliminated and the relative locations of the machines and cells are determined. Moreover, a genetic algorithm as an efficient meta-heuristic algorithm...

... periods. Because n remains constant as time passes, for the next forecast, a new (most recent) period data point is included, and the “oldest” data point is excluded from the calculation. Forecasting Soft computing Fuzzy inference system Neural network Genetic algorithm...

..., a detailed computational analysis is undertaken to identify the conditions under which a flexible plant is preferred over a dedicated plant. A combination of genetic algorithm and sample-based optimization procedure is used to capture the effects of preset service level. The factors controlled in this paper...

... traveling cost and the time window violations. Two meta-heuristic algorithms, namely, simple genetic algorithm (GA) and hybrid genetic algorithm (HGA) are used to find the best solution for this problem. A comparison on the results of these two algorithms has been done and based on the outcome, it has been...

.... Genetic algorithm is used to determine the optimal candidate among the experts to be addressed at every feedback step. Ordinal (rank) expert evaluation is used for decision-making in many weakly-structured human activity scopes (business, state government, scientific planning, etc.). Ordinal expert...