Emilia Golemanova
ABSTRACT
The paper describes how the built-in tools for stochastic dynamic control of the computation process in a programming paradigm, named Control Network Programming (CNP), could be used to achieve declarative (non-procedural) implementations of a genetic algorithm. As these implementations are very intuitive and easily programmed, CNP can be used as an excellent approach for teaching, learning and programming the basic model of the genetic algorithms. They are presented on the well-known 8-queens problem often used as an example problem for various programming techniques, including non-traditional approaches such as genetic algorithms. More specifically, the emphasis is on automatic, non-procedural modelling of certain selection operators such as roulette wheel selection and rank selection, as well as the Bernoulli trial, used in crossover and mutation operators.
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Digital Library
- K. Kratchanov, T. Golemanov, E. Golemanova, and T. Ercan, "Control Network Programming with SPIDER: Dynamic Search Control," in 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2010), 2010, pp. 253--262. Google ScholarDigital Library
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Index Terms
- Declarative Implementations of Genetic Algorithms in Control Network Programming
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