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Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is effective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

This paper addresses the problem of optimal trajectory planning for Stewart platform based machine tools. The objectives are to maximize the stiffness of the structure and to minimize the force requirement of the actuators while satisfying the constraints of workspace and singularity. A computational algorithm for searching the optimum location of machining trajectory is developed. The algorithm is applied to two contour machining examples involving end milling. The simulation results of contour machining along the optimal trajectory are discussed.

Purpose Wheelchair users face great difficulty in transferring themselves from one surface to another, for example from wheelchair to a toilet commode. In such cases, mostly a caregiver’s assistance may be required, but it affects one’s dignity. The purpose of this paper is to develop a robotic self-transfer device, which is aimed at offering privacy and independence to people with lower limb disabilities in performing daily activities. Design/methodology/approach The device, attached to a powered wheelchair, is useful in transferring a user from a wheelchair to a toilet commode or any other surface following simple and natural transfer procedure without the need of any caregiver. The user can achieve transfer by operating joysticks. The device employs two linear actuators and a motor to accomplish the transfer. Trials were carried out to test the performance of the device by involving potential beneficiaries. Findings The device could successfully transfer the participants from a wheelchair to a chair with less effort in less than a minute. The results of the trials show that the participants felt comfortable in using the device. It was also found that the device is superior to other existing transfer systems in terms of comfort and operation. Originality/value The existing self-transfer systems are alternative solutions that serve the purpose of mobility coupled with self-transfer. Instead of developing an alternative mobility solution, this paper proposes a novel design of a self-transfer device that can be used as an attachment to wheelchair.