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Biomedical subjects

J M McNamara

Publications and source records attributed to J M McNamara.

6 recordsLinked to original sources
PubMed1990

Genetic algorithms and evolution.

The genetic algorithm (GA) as developed by Holland (1975, Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press) is an optimization technique based on natural selection. We use a modified version of this technique to investigate which aspects of natural selection make it an efficient search procedure. Our main modification to Holland's GA is the subdividing of the population into semi-isolated demes. We consider two examples. One is a fitness landscape with many local optima. The other is a model of singing in birds that has been previously analysed using dynamic programming. Both examples have epistatic interactions. In the first example we show that the GA can find the global optimum and that its success is improved by subdividing the population. In the second example we show that GAs can evolve to the optimal policy found by dynamic programming.

Algorithms↗
PubMed1990

The value of fat reserves and the tradeoff between starvation and predation.

It is shown that in a range of models, the probability that a forager dies from starvation is, to a good approximation, an exponential function of energy reserves. Using a time and energy budget for a 19g passerine, we explore the consequences, in terms of starvation and predation, of various levels of energy reserves. It is shown that there exists an optimal level L* of reserves at which total mortality (starvation plus predation) is minimized. L* increases when the environment deteriorates as a result of a decrease in either temperature or mean gross gain or an increase in the mean search time. The effect of combined deteriorations is greater than the sum of their individual effects. At L*, the probability of predation is much higher than the probability of starvation. A simple analytic model suggests that this result will be fairly general, but also indicates conditions under which the result might not hold.

Adipose Tissue↗
PubMed1990

Markers of serious illness in infants under 6 months old presenting to a children's hospital.

Six hundred and eighty two assessments were performed on 641 babies under 6 months of age who presented to the emergency department of the Royal Children's Hospital, Melbourne, to try and determine the best markers of serious illness in young infants. Detailed, specific questions that quantified a baby's functional response to illness gave the most useful information. As a group, the six most common predictive symptoms of serious illness were: taking less than half the normal amount of feed over the preceding 24 hours, breathing difficulty, having less than four wet nappies in the preceding 24 hours, decreased activity, drowsiness, and a history of being both pale and hot. The presence of the corresponding sign on examination increased the predictive value of the symptom by 10-20%. Specific, highly predictive (though less common) signs included moderate to severe chest wall recession, respiratory grunt, cold calves, and a tender abdomen. A list of low, medium, and high risk symptoms has been constructed and the five measurements that were most useful in predicting serious illness in young infants have been detailed.

Causality↗
PubMed1987

Memory and the efficient use of information.

We consider the problem of how an animal's memory should be designed in order to cope with a stochastic and changing environment. In particular we consider the problem of forming the best estimate of an unknown and possibly changing environmental parameter. Under the simple model we consider, the effect of an observation is to update this estimate using a linear operator. Two models of a changing environment are analysed. For each model we show how estimates change as a function of time elapsed and observations taken. The effect of a regular sequence of observations is also considered, and it is shown that an exponential weighting of past observations is a sufficient statistic on which to base decisions. The weighting factors are different in the two model environments considered, but each is shown to be a function of the rate at which the environment is changing.

Animals↗
PubMed1987

A general framework for understanding the effects of variability and interruptions on foraging behaviour.

A general framework for analysing the effects of variability and the effects of interruptions on foraging is presented. The animal is characterised by its level of energetic reserves, x. We consider behaviour over a period of time [O,T]. A terminal reward function R(x) determines the expected future reproductive success of an animal with reserves x at time T. For any state x at a time in the period, we give the animal a choice between various options and then constrain it to follow a background strategy. The best option is the one that maximizes expected future reproductive success. Using this framework, we show that sensitivity to variability in amount of energy gained is logically distinct from sensitivity to variability in the time at which food is obtained. We also show that incorporating interruptions results in both a preference for variability in time and a preference for a reward followed by a delay as opposed to the same delay before the reward.

Animals↗