Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data
Pattern Recognition Letters - special issue on pattern recognition in practice V
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
An artificial immune network for multimodal function optimization on dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
An Immune-Inspired Approach to Bayesian Networks
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Tightness time for the linkage learning genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
MOBAIS: A Bayesian Artificial Immune System for Multi-Objective Optimization
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Baldwinian learning in clonal selection algorithm for optimization
Information Sciences: an International Journal
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
A novel Artificial Immune System for fault behavior detection
Expert Systems with Applications: An International Journal
Artificial immune multi-objective SAR image segmentation with fused complementary features
Information Sciences: an International Journal
Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs
Information Sciences: an International Journal
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
Evaluating the performance of a Bayesian Artificial Immune System for designing fuzzy rule bases
International Journal of Hybrid Intelligent Systems
Hi-index | 0.07 |
Significant progress has been made in theory and design of Artificial Immune Systems (AISs) for solving hard problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AISs to deal effectively with building blocks (partial high-quality solutions coded in the antibody). The available AISs present mechanisms for evolving the population that do not take into account the relationship among the variables of the problem, potentially causing the disruption of high-quality partial solutions. This paper proposes a novel AIS with abilities to identify and properly manipulate building blocks in optimization problems. Instead of using cloning and mutation to generate new individuals, our algorithm builds a probabilistic model representing the joint probability distribution of the promising solutions and, subsequently, uses this model for sampling new solutions. The probabilistic model used is a Bayesian network due to its capability of properly capturing the most relevant interactions among the variables. Therefore, our algorithm, called Bayesian Artificial Immune System (BAIS), represents a significant attempt to improve the performance of immune-inspired algorithms when dealing with building blocks, and hence to solve efficiently hard optimization problems with complex interactions among the variables. The performance of BAIS compares favorably with that produced by contenders such as state-of-the-art Estimation of Distribution Algorithms.