Communicating sequential processes
Communicating sequential processes
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The problem with solutions to the frame problem
The robot's dilemma revisited
Communicating and mobile systems: the &pgr;-calculus
Communicating and mobile systems: the &pgr;-calculus
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Concurrent Programming in Java. Second Edition: Design Principles and Patterns
Concurrent Programming in Java. Second Edition: Design Principles and Patterns
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Formal verification to enhance evolution of protocols
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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As mobile devices become more powerful, interprocess communication becomes increasingly more important. Unfortunately, this larger freedom of mobility gives rise to unknown environments. In these environments, processes that want to communicate with each other will be unable to do so because of protocol conflicts. Although conflicting protocols can be remedied by using adaptors, the number of possible combinations of different protocols increases dramatically. Therefore we propose a technique to generate protocol adaptors automatically. This is realised by means of genetically engineered classifier systems that use Petri nets as a specification for the underlying protocols. This paper reports on an experiment that validates this approach.