An agent-based model of hierarchic genetic search

DOI10.1016/j.camwa.2012.02.052zbMath1268.68141DBLPjournals/cma/SchaeferBKS12OpenAlexW2075414703WikidataQ96309129 ScholiaQ96309129MaRDI QIDQ356405

Aleksander Byrski, MacIej Smołka, Robert Schaefer, Joanna Kolodziej

Publication date: 25 July 2013

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2012.02.052

zbMATH Keywords

genetic algorithmsglobal optimisationhierarchic genetic strategymulti-agent genetic system

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (5)

Understanding measure-driven algorithms solving irreversibly ill-conditioned problems ⋮ A multiagent evolutionary algorithm with direct and indirect combined representation for constraint satisfaction problems ⋮ A security framework in G-Hadoop for big data computing across distributed cloud data centres ⋮ The island model as a Markov dynamic system ⋮ Accelerating backtrack search with a best-first-search strategy

Cites Work

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• Modeling genetic algorithms with Markov chains.
• Foundations of global genetic optimization. With contribution by Henryk Telega.
• Stochastic Model of Evolutionary and Immunological Multi-Agent Systems: Mutually Exclusive Actions
• Stochastic Model of Evolutionary and Immunological Multi-Agent Systems: Parallel Execution of Local Actions
• Theory of genetic algorithms

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