C K Mohan | PubMed Health

PubMed2000

Adaptive linkage crossover.

Problem-specific knowledge is often implemented in search algorithms using heuristics to determine which search paths are to be explored at any given instant. As in other search methods, utilizing this knowledge will more quickly lead a genetic algorithm (GA) towards better results. In many problems, crucial knowledge is not found in individual components, but in the interrelations between those components. For such problems, we develop an interrelation (linkage) based crossover operator that has the advantage of liberating GAs from the constraints imposed by the fixed representations generally chosen for problems. The strength of linkages between components of a chromosomal structure can be explicitly represented in a linkage matrix and used in the reproduction step to generate new individuals. For some problems, such a linkage matrix is known a priori from the nature of the problem. In other cases, the linkage matrix may be learned by successive minor adaptations during the execution of the evolutionary algorithm. This paper demonstrates the success of such an approach for several problems.

Algorithms↗

PubMed1992

Prediction criteria for successful weaning from respiratory support: statistical and connectionist analyses.

OBJECTIVE: To develop predictive criteria for successful weaning of patients from mechanical assistance to ventilation, based on simple clinical tests using discriminant analyses and neural network systems. DESIGN: Retrospective development of predictive criteria and subsequent prospective testing of the same predictive criteria. SETTING: Medical ICU of a 300-bed teaching Veterans Administration Hospital. PATIENTS: Twenty-five ventilator-dependent elderly patients with acute respiratory failure. INTERVENTIONS: Routine measurements of negative inspiratory force, tidal volume, minute ventilation, respiratory rate, vital capacity, and maximum voluntary ventilation, followed by a weaning trial. Success or failure in 21 efforts was analyzed by a linear and quadratic discriminant model and neural network formulas to develop prediction criteria. The criteria developed were tested for predictive power prospectively in nine trials in six patients. RESULTS: The statistical and neural network analyses predicted the success or failure of weaning within 90% to 100% accuracy. CONCLUSION: Use of quadratic discriminant and neural network analyses could be useful in developing accurate predictive criteria for successful weaning based on simple bedside measurements.

Aged↗