Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
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The Simple Genetic Algorithm: Foundations and Theory
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Operating System Concepts with Java
Asymptotic analysis of computational multi-agent systems
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Future Generation Computer Systems
An evolutionary multi-agent approach to anomaly detection and cyber defense
Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
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An effective exploration of the large search space by single population genetic-based metaheuristics may be a very time consuming and complex process, especially in the case of dynamic changes in the system states. Speeding up the search process by the metaheuristic parallelisation must have a significant negative impact on the search accuracy. There is still a lack of complete formal models for parallel genetic and evolutionary techniques, which might support the parameter setting and improve the whole (often very complex) structure management. In this paper, we define a mathematical model of Hierarchical Genetic Search (HGS) based on the genetic multi-agent system paradigm. The model has a decentralised population management mechanism and the relationship among the parallel genetic processes has a multi-level tree structure. Each process in this tree is Markov-type and the conditions of the commutation of the Markovian kernels in HGS branches are formulated.