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We introduce a framework, called " physicomimetics, " that provides distributed control of large collections of mobile physical agents in sensor networks. The agents sense and react to virtual forces, which are motivated by natural physics laws. Thus, physicomimetics is founded upon solid scientific principles. Furthermore, this framework provides an(More)
In this paper we explore the use of an adaptive search technique (genetic algorithms) to construct a system GABEL which continually learns and refines concept classification rules from its interaction with the environment. The performance of the system is measured on a set of concept learning problems and compared with the performance of two existing(More)
In this paper, we explore the use of genetic algorithms (GAs) as a key element in the design and implementation of robust concept learning systems. We describe and evaluate a GA-based system called GABIL that continually learns and refines concept classification rules from its interaction with the environment. The use of GAs is motivated by recent studies(More)
A strategy for using Genetic Algorithms (GAs) to solve NP-complete problems is presented. The key aspect of the approach taken is to exploit the observation that, although all NP-complete problems are equally difficult in a general computational sense, some have much better GA representations than others , leading to much more successful use of GAs on some(More)
Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 cross-over points for strings of length L. Theoretical results(More)
On the basis of early theoretical and empirical studies. genetic algorithms have typically used I and Z-point crossover operators as the standard mechanisms for implementing recombination. However. there have been a number of recent studies. primarily empirical in nature, which have shown the benefits or crossover operators involving a higher number of(More)
In this paper we develop an empirical methodology for studying the behavior of evolutionary algorithms based on problem generators. We then describe three generators that can be used to study the effects of epistasis on the performance of EAs. Finally, we illustrate the use of these ideas in a preliminary exploration of the effects of epistasis on simple(More)