Abstract
In this chapter, we will discuss how evolutionary methods can be used for test generation of digital circuits. In present time it is strongly investigated the new direction in theory and practice of artificial intelligence and information systems – evolutionary computations. This term is used to generic description of the search, optimizing or learning algorithms, based on some formal principles of natural evolutional selection, which are sufficiently applied in solving various problems of machine learning, data mining, databases etc [1]. Among this approaches following main paradigms can be picked out: genetic algorithms (GA), evolutionary strategy (ES), evolutional programming (EP), genetic programming (GP). The differences of these approaches mainly consist in the way of target solution representation and in different set of evolutional operators used in evolutional simulation. Classical GA uses the binary encoding of problem solution and basic genetic operators are crossover and mutation. In ES solution is represented by real numbers vector and basic operator is mutation. EP uses FSM as solution representation and mutation operator. In GP problem solution is represented by program, crossover and mutation operators are applied. Now this classification is enough relative and interaction of basic evolutionary paradigms each other takes place.
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Skobtsov, Y.A., Skobtsov, V.Y. (2011). 13 Evolutionary Test Generation Methods for Digital Devices. In: Adamski, M., Barkalov, A., Węgrzyn, M. (eds) Design of Digital Systems and Devices. Lecture Notes in Electrical Engineering, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17545-9_13
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