Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing many of the problems of complex system design and implementation that motivated increasing software localization in the first place. Naturally occurring systems of simple agents (such as populations of insects or other animals) suggest that this retreat is not necessary. This paper summarizes several studies of such systems, and derives from them a set of general principles that artificial multi-agent systems can use to support overall system behavior significantly more complex than the behavior of the individuals agents. 1.

Abstract — Spatial data refer to any data about objects that occupy real physical space. Attributes within spatial databases usually include spatial information. Spatial data refers to the numerical or categorical values of a function at different spatial locations. Spatial metadata refers to the descriptions of the spatial configuration. Application of classical association rule mining concepts to spatial databases is promising but very challenging. Spatial Association Rule Mining requires new approaches compared to classical association rule mining. Spatial data consists of dependent events compared to transactional data which consist of independent transactions. It is more difficult to classify a discovered spatial association rule as interesting. Instead of much generalized rule more specific rule discovery needs further research. Spatial Association Rules are association rules about spatial data objects. Either the antecedent or the consequent of the rule must contain some spatial predicates. Spatial association rules are implications of one set of data by another. The main area of concentration in this paper is to optimize the rules generated by Association Rule Mining (Apriori method), using hybrid evolutionary algorithm. The main motivation for using Evolutionary algorithms in the discovery of high-level prediction rules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithms often used in data mining. The improvements applied in EAs are reflected in the rule based systems used for classification as described in results and conclusions. The future enhancements will be on using the other Evolutionary Optimization Algorithms such as PSO (Particle Swarm Optimization) for the rule generation.

Multi-label spatial classification based on association rules with multi objective genetic algorithms (MOGA) enriched by semi supervised learning is proposed in this paper. It is to deal with multiple class labels problem. In this paper we adapt problem transformation for the multi label classification. We use hybrid evolutionary algorithm for the optimization in the generation of spatial association rules, which addresses single label. MOGA is used to combine the single labels into multi labels with the conflicting objectives predictive accuracy and comprehensibility. Semi supervised learning is done through the process of rule cover clustering. Finally associative classifier is built with a sorting mechanism. The algorithm is simulated and the results are compared with MOGA based associative classifier, which out performs the existing. Keywords: