Biomedical subjects

B H Sumida

Publications and source records attributed to B H Sumida.

3 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↗

PubMed1987

Relative allocation on concurrent schedules can depend on schedule parameters when behavioral parameters are constant.

We develop a simple model of switching between the initial links of a concurrent-chain procedure. Behavior is determined by four parameters mu(1), mu(2), q(1), and q(2). The first two are the basic rates of switching from Schedule 1 and Schedule 2, respectively. The second two are the probabilities of leaving Schedule 1 and Schedule 2 after the corresponding terminal link has been completed. We show that for fixed values of these four parameters, the relative allocation on the initial links may change as a result of changes in initial-link schedules. The effect can be quite large if the switching rates are low. An implication is that relative allocation is not necessarily a good measure of behavior.

Journal Article↗

PubMed1987

The maximization of overall reinforcement rate on concurrent chains.

We model behavioral allocation on concurrent chains in which the initial links are independent variable-interval schedules. We also quantify the relationship between behavior during the initial links and the probability of entering a terminal link. The behavior that maximizes overall reinforcement rate is then considered and compared with published experimental data. Although all the trends in the data are predicted by rate maximization, there are considerable deviations from the predictions of rate maximization when reward magnitudes are unequal. We argue from our results that optimal allocation on concurrent chains, and prey choice as used in the theory of optimal diets, are distinct concepts. We show that the maximization of overall rate can lead to apparent violations of stochastic transitivity.

Journal Article↗