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Theodore C. Belding

Publications and source records attributed to Theodore C. Belding.

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Nobility and Stupidity: Modeling the Evolution of Class Endogamy

Class endogamy is a phenomenon in which nobles only marry other nobles and commoners only marry other commoners. The origin of class endogamy, and of social stratification in general, is a major open question in archaeology. This paper implements a verbal model proposed by Marcus and Flannery as a class of agent-based computer models by generalizing and simplifying a mathematical model of marriage markets developed by Burdett and Coles. One force that can produce class endogamy occurs if agents are only willing to marry suitors having status no less than some fixed value below the status of their highest-status suitor, which they can learn. Another such force results if children inherit the average of their parents' statuses. In contrast, status achieved over an agent's lifetime can be viewed as noise, analogous to mutation in biological evolution. I propose that class endogamy may have resulted from the interaction of forces such as these, along with other factors such as ideology. Simulation results are presented, and potential areas for future research are sketched out. The validity of these models for any particular culture depends, of course, on whether these forces were actually operating in that society.

nlin.AO

Potholes on the Royal Road

It is still unclear how an evolutionary algorithm (EA) searches a fitness landscape, and on what fitness landscapes a particular EA will do well. The validity of the building-block hypothesis, a major tenet of traditional genetic algorithm theory, remains controversial despite its continued use to justify claims about EAs. This paper outlines a research program to begin to answer some of these open questions, by extending the work done in the royal road project. The short-term goal is to find a simple class of functions which the simple genetic algorithm optimizes better than other optimization methods, such as hillclimbers. A dialectical heuristic for searching for such a class is introduced. As an example of using the heuristic, the simple genetic algorithm is compared with a set of hillclimbers on a simple subset of the hyperplane-defined functions, the pothole functions.

cs.NE

Numerical Replication of Computer Simulations: Some Pitfalls and How To Avoid Them

A computer simulation, such as a genetic algorithm, that uses IEEE standard floating-point arithmetic may not produce exactly the same results in two different runs, even if it is rerun on the same computer with the same input and random number seeds. Researchers should not simply assume that the results from one run replicate those from another but should verify this by actually comparing the data. However, researchers who are aware of this pitfall can reliably replicate simulations, in practice. This paper discusses the problem and suggests solutions.

nlin.AO

The Distributed Genetic Algorithm Revisited

This paper extends previous work done by Tanese on the distributed genetic algorithm (DGA). Tanese found that the DGA outperformed the canonical serial genetic algorithm (CGA) on a class of difficult, randomly-generated Walsh polynomials. This left open the question of whether the DGA would have similar success on functions that were more amenable to optimization by the CGA. In this work, experiments were done to compare the DGA's performance on the Royal Road class of fitness functions to that of the CGA. Besides achieving superlinear speedup on KSR parallel computers, the DGA again outperformed the CGA on the functions R3 and R4 with regard to the metrics of best fitness, average fitness, and number of times the optimum was reached. Its performance on R1 and R2 was comparable to that of the CGA. The effect of varying the DGA's migration parameters was also investigated. The results of the experiments are presented and discussed, and suggestions for future research are made.

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