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
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks
Information Sciences: an International Journal
A population adaptive based immune algorithm for solving multi-objective optimization problems
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Omni-aiNet: an immune-inspired approach for omni optimization
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Towards an artificial immune system for online fraud detection
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Evaluating the performance of a Bayesian Artificial Immune System for designing fuzzy rule bases
International Journal of Hybrid Intelligent Systems
Hi-index | 0.00 |
Significant progress has been made in theory and design of artificial immune systems (AISs) for solving multi-objective problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AIS to deal effectively with building blocks (high-quality partial 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, causing the disruption of these high-quality partial solutions. Recently, we proposed a novel immune-inspired approach for single-objective optimization as an attempt to avoid this drawback. Our proposal replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network representing the joint distribution of promising solutions and, subsequently, uses this model for sampling new solutions. Now, in this paper we extend our methodology for solving multi-objective optimization problems. The proposal, called Multi-Objective Bayesian Artificial Immune System (MOBAIS), was evaluated in the well-known multi-objective Knapsack problem and its performance compares favorably with that produced by contenders such as NSGA-II, MISA, and mBOA.