"Indirectly, ε and ε c thresholds have a strong influence over the number of decision cells finally presented to decision-maker. For the purchasing problem explored in previous work  with cells of size ten receptors, we have analyzed the influence of ε and ε c thresholds over the number of cells generated. This work adds a new filter – the weighted Euclidian distance – to this selection stream in order to provide a more consistent set of cells to decisionmaker . "
[Show abstract][Hide abstract] ABSTRACT: This paper presents an enhanced version of the AED (Appropriate Executive Decisions) algorithm, which is based on biological immune system (BIS) and whose purpose is the generation of appropriate executive decisions aimed at business environments. A new metric has been incorporated to the algorithm and a larger and more representative database was used to train and validate results. Moreover, this paper offers better directions on how to apply AED in executive decisions, affording the learning process quality improvement through immunoinformatics concepts, namely decision cells, thereby producing more appropriate executive decisions. Experiments were carried out with executive officers experienced in executive decisions in order to suitably validate the appropriateness of responses generated by the enhanced AED algorithm.
Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on; 10/2008
[Show abstract][Hide abstract] ABSTRACT: This paper presents a novel approach to support appropriateness of executive decisions by using combined principles of Artificial Immune System (AIS) and Fuzzy Logic (FL). The main goal is to show that more Appropriate Executive Decisions (AED) may be obtained if strategic decision makers are equipped with supportive tools based on AIS and FL (Fuzzy-AED). In addition, this work aims at improving the quality of the system response by adding new features to the original version of the AED model. A proof of concept for Fuzzy-AED is also included here along with experiments carried out within a real business environment. Experimental results suggest that this hybrid approach to executive decision making could be used to assemble helpful executive decision systems that may be easily deployed to reduce some of the risks inherent in strategic decision making.
10th Brazilian Symposium on Neural Networks (SBRN 2008), Salvador, Bahia, Brazil, October 26-30, 2008; 01/2008
[Show abstract][Hide abstract] ABSTRACT: The immune system is a remarkable information processing and self learning system that offers inspiration to build artificial immune system (AIS). The field of AIS has obtained a significant degree of success as a branch of Computational Intelligence since it emerged in the 1990s. This paper surveys the major works in the AIS field, in particular, it explores up-to-date advances in applied AIS during the last few years. This survey has revealed that recent research is centered on four major AIS algorithms: (1) negative selection algorithms; (2) artificial immune networks; (3) clonal selection algorithms; (4) Danger Theory and dendritic cell algorithms. However, other aspects of the biological immune system are motivating computer scientists and engineers to develop new models and problem solving methods. Though an extensive amount of AIS applications has been developed, the success of these applications is still limited by the lack of any exemplars that really stand out as killer AIS applications.
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