By Zili Zhang, Chengqi Zhang
Solving advanced difficulties in real-world contexts, resembling monetary funding making plans or mining huge information collections, comprises many alternative sub-tasks, every one of which calls for various concepts. to house such difficulties, a very good variety of clever ideas can be found, together with conventional strategies like specialist platforms ways and delicate computing suggestions like fuzzy common sense, neural networks, or genetic algorithms. those options are complementary techniques to clever details processing instead of competing ones, and therefore greater leads to challenge fixing are completed while those concepts are mixed in hybrid clever structures. Multi-Agent structures are preferrred to version the manifold interactions one of the varied elements of hybrid clever systems.
This publication introduces agent-based hybrid clever platforms and provides a framework and method making an allowance for the advance of such structures for real-world purposes. The authors specialize in purposes in monetary funding making plans and information mining.
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4 Agent-Oriented Methodologies Developing applications in terms of autonomous software agents that exhibit proactive and intelligent behavior, and that interact with one another in terms of high-level protocols and languages, leads to a new programming paradigm. By dint of being a new programming paradigm, the development of agentbased applications implies new programming abstractions and techniques, as well as new methodologies. Agent-based systems for complex problem solving and decision making, like hybrid intelligent systems, usually have a large number of parts or components that have many interactions.
It is impossible to embed many intelligent techniques within a single agent. Otherwise, the agents will be overloaded. In many applications, the agents in multi-agent systems should be kept simple for ease of maintenance, initialization, and customization. 7 Agent-Based Hybrid Systems: State of the Art • • 39 It is not ﬂexible to add more intelligent techniques to, or delete some unwanted one from, the multi-agent systems. For example, one software agent may be equipped with fuzzy logic, the other with a neural network etc.
The basic for evolutionary computing algorithms is biological evolution, where over time evolution produces the best or ‘ﬁttest’ individuals. Chromosomes, which are DNA strings, provide the abstract model for a living organism. Subsections of the chromosomes, which are called genes, are used to deﬁne diﬀerent traits of the individual. During reproduction, genes from parents are combined to produce the genes for child. When using genetic algorithms to solve a problem, the ﬁrst thing, and perhaps the most diﬃcult task, which must be determined is how to model the problem as a set of individuals.