Biomedical subjects

K Deb

Publications and source records attributed to K Deb.

5 recordsLinked to original sources

PubMed2001

Safety and efficacy of peribulbar block as adjunct to general anaesthesia for paediatric ophthalmic surgery.

METHODS: Fifty children (age 5-14 years, ASA I-II) undergoing elective ophthalmic surgery were chosen for the study. Of these, 25 received intravenous pethidine (control group) and 25 received a peribulbar block (block group) for perioperative analgesia, and were monitored intraoperatively and postoperatively by an investigator blinded to the analgesic technique. RESULTS: Intraoperative values of haemodynamic variables were significantly higher in the control group (P < 0.01). Requirement for intraoperative rescue analgesic and postoperative analgesia was higher in the control group (P < 0.05 and P < 0.001, respectively). Children in the block group had lower postoperative pain scores at all times. Incidence of oculocardiac reflex was significantly higher (P < 0.001) in the control group. Seventy-six percent of children in the control group had postoperative nausea and vomiting compared to 20% children in the block group (P < 0.001). CONCLUSION: There were no complications related to the block. Peribulbar block appears to be a safe and useful analgesic technique for paediatric ophthalmic surgery.

Adolescent↗

PubMed2001

Self-adaptive genetic algorithms with simulated binary crossover.

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs.

Algorithms↗

PubMed2000

Comparison of multiobjective evolutionary algorithms: empirical results.

In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.

Algorithms↗

PubMed1999

Multi-objective genetic algorithms: problem difficulties and construction of test problems.

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.

Algorithms↗

PubMed1998

Time scheduling of transit systems with transfer considerations using genetic algorithms.

Scheduling of a bus transit system must be formulated as an optimization problem, if the level of service to passengers is to be maximized within the available resources. In this paper, we present a formulation of a transit system scheduling problem with the objective of minimizing the overall waiting time of transferring and nontransferring passengers while satisfying a number of resource- and service-related constraints. It is observed that the number of variables and constraints for even a simple transit system (a single bus station with three routes) is too large to tackle using classical mixed-integer optimization techniques. The paper shows that genetic algorithms (GAs) are ideal for these problems, mainly because they (i) naturally handle binary variables, thereby taking care of transfer decision variables, which constitute the majority of the decision variables in the transit scheduling problem; and (ii) allow procedure-based declarations, thereby allowing complex algorithmic approaches (involving if then-else conditions) to be handled easily. The paper also shows how easily the same GA procedure with minimal modifications can handle a number of other more pragmatic extensions to the simple transit scheduling problem: buses with limited capacity, buses that do not arrive exactly as per scheduled times, and a multiple-station transit system having common routes among bus stations. Simulation results show the success of GAs in all these problems and suggest the application of GAs in more complex scheduling problems.

Algorithms↗