Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
An artificial immune network for multimodal function optimization on dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Omni-optimizer: a procedure for single and multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
MOBAIS: A Bayesian Artificial Immune System for Multi-Objective Optimization
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
A Multi-Objective Multipopulation Approach for Biclustering
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Evolving phylogenetic trees: a multiobjective approach
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
A diversity preserving selection in multiobjective evolutionary algorithms
Applied Intelligence
Neural network ensembles: immune-inspired approaches to the diversity of components
Natural Computing: an international journal
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
A multi-objective artificial immune system based on hypervolume
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
A survey on optimization metaheuristics
Information Sciences: an International Journal
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This work presents omni-aiNet, an immune-inspired algorithm developed to solve single and multi-objective optimization problems, either with single and multi-global solutions. The search engine is capable of automatically adapting the exploration of the search space according to the intrinsic demand of the optimization problem. This proposal unites the concepts of omni-optimization, already proposed in the literature, with distinctive procedures associated with immune-inspired concepts. Due to the immune inspiration, the omni-aiNet presents a population capable of adjusting its size during the execution of the algorithm, according to a predefined suppression threshold, and a new grid mechanism to control the spread of solutions in the objective space. The omni-aiNet was applied to several optimization problems and the obtained results are presented and analyzed.