This paper presents a hybrid genetic algorithm approach to construct optimal polynomial expressions to characterise a function described by a set of data points.
This study presents a hybrid genetic algorithm approach to the problems of assembly planning with various objectives, including minimizing cycle time, maximizing workload smoothness, minimizing the frequency of tool change, minimizing the number of tools and machines used, and minimizing the complexity of assembly sequences.
In another study, a hybrid genetic algorithm approach to the assembly line planning problem was developed (Chen et al. 2002).
This study incorporates desirability functions into a hybrid neural network/genetic algorithm approach to optimize the parameter design of dynamic multiresponse with continuous values of parameters.
The solutions yielded by the exact optimization technique are compared with those of the hybrid algorithm in general approach.
The hybrid algorithm fuses two approaches based on two conditions in a sequential manner.
On the electrode level, we develop approximate analytical solutions for the 1D current/potential distribution via a hybrid algorithm of power-law approach and perturbation method.
Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers.
This paper presents a hybrid Hopfield network-genetic algorithm approach to the cell-to-switches assignment problem, in which a Hopfield network manages the problem's constraints, and a genetic algorithm searches for high quality solutions with the minimum possible cost in terms of handoff and cable displayed.
The faster speed from the hybrid algorithm offers a promising approach for real-time clinical analysis.
This hybrid algorithm combines advantages from both approaches: it guarantees deterministic termination if the failure detector is accurate, and probabilistic termination otherwise.
