Task differentiation in Polistes wasp colonies: a model for self-organizing groups of robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Swarm intelligence
Wasp nests for self-configurable factories
Proceedings of the fifth international conference on Autonomous agents
Ant Colony Optimization
Community detection in social networks with genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
GA-Net: A Genetic Algorithm for Community Detection in Social Networks
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Hierarchical Classification Ant Colony Algorithm for Predicting Gene Ontology Terms
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Swarm Intelligence for Analyzing Opinions in Online Communities
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
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Modern software systems must be continuously adapted to current performance and usability requirements. Indicators like overhead, computational complexity, parameter tuning, or ease of design and implementation are getting increasingly harder to accomplish due to constant increase in system dimensions like code size, API (Application Programming Interface), deployment size, component communication, network lag etc. Furthermore, many entities rely on classic, highly deterministic algorithms that are little or not capable of changing strategies on the fly. Lately, bio-inspired algorithms have successfully tackled this problem with significant, positive results. We propose a framework that may prove useful in obtaining better performance by automatically selecting and combining the best swarm intelligence algorithms with the best parameter selection.