Abstract
The study focuses on two operators of a genetic algorithm (GA): a crossover and a mutation in the context of machine learning of fuzzy logic rules. A decision support system (DSS) is placed in a simulation environment created in accordance with the complex adaptive system (CAS) concept. In a multi-agent CAS system, the learning classifier system (LCS) paradigm is used to develop a learning system. The aim of the learning system is to discover binary rules that allow an agent to perform efficient actions in a simulation environment. The agent’s objective is to make an effective decision on which order, from the set of the awaiting orders, should be transferred into a production zone next. The decision is based on the fuzzy logic system response. In the conducted study, two input signals and one output signal of the fuzzy logic system are considered. The concept of the presented fuzzy logic system affects the construction of rules of a specific agent. The paper focuses on the problem of coding the agent’s rules and modification of the coding by the GA.
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Skobiej, B., Jardzioch, A. (2019). Selected Aspects of Crossover and Mutation of Binary Rules in the Context of Machine Learning. In: Świątek, J., Borzemski, L., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018. ISAT 2018. Advances in Intelligent Systems and Computing, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-99996-8_34
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